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The Modern Design of Experiments A Knowledge-Management Approach to Aerospace Testing

The Modern Design of Experiments A Knowledge-Management Approach to Aerospace Testing. Richard DeLoach NASA Langley Research Center. Presentation to the Delta Forum 44 th AIAA Aerospace Sciences Meeting & Exhibit Reno, NV – January 10, 2006.

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The Modern Design of Experiments A Knowledge-Management Approach to Aerospace Testing

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  1. The Modern Design of Experiments A Knowledge-Management Approach to Aerospace Testing Richard DeLoach NASA Langley Research Center Presentation to the Delta Forum 44th AIAA Aerospace Sciences Meeting & Exhibit Reno, NV – January 10, 2006

  2. Exploiting the Difference Between “Data” and “Knowledge”A specific example from experimental aeronautics • Quality and productivity in experimental aeronautics have traditionally focused on data as the primary product of an empirical study. • A productive test maximizes the volume of data • A high-quality test generatesdata with a minimum of unexplained variance • NASA Langley Research Center has introduced a testing process called the Modern Design of Experiments (MDOE) that focuses on knowledge as the product of experimentation rather than data. • Tactical objectives change from data collection to enhancing knowledge of the system under study • This radically changes how we assess productivity and quality.

  3. The Modern Design of ExperimentsA Knowledge Management Approach to Scientific Experimentation • Knowledge of a system is said to be attained in an MDOE test when all system responses of interest can be adequately predicted. • “Adequately” implies acceptable precision over the full range of independent variables of interest (not limited only to variable combinations measured.) • Example: When the drag coefficient of a commercial jet transport can be estimated within ±0.00005 with 95% confidence for Mach numbers between 0.70 and 0.96, and angles of attack between -4° and +10°.

  4. The Modern Design of ExperimentsA “knowledge not data” focus changes how we regard quality and productivity • A productive MDOE testminimizes the data volume required to adequately predict system responses. • This minimizes cycle time and direct operating costs • Opposite of traditional productivity metrics that promote high data volume. • A high-quality MDOE result features high precision in the system response predictions • Dataquality is only a cost issue: high variability simply requires more data • Quality results can be achieved no matter how much variability in the data Savings of a factor of two in cost and cycle time are not uncommon with this change in focus from data collection to knowledge enhancement.

  5. Resource Savings MDOE vs. Conventional Test Processes Representative MDOE Resource SavingsFocusing on Knowledge rather than Data • Independent comparisons of MDOE and conventional test processes were conducted over a 12-month period • Wind tunnel tests were conducted in multiple facilities • Subsonic, transonic, supersonic flight regimes • Resource comparisons were made for wind-on minutes, megawatt hours, and total data volume.

  6. A Parting Thought • Based on balance of trade data, the world especially values two American products* • Aerospace systems • Wheat • The one we most efficiently cultivate is wheat. • Knowledge management will become increasingly important for aerospace research because... • The shift in focus from data collection to knowledge enhancement is key to cultivating aerospace systems as efficiently as wheat. *Credit: Dennis Bushnell, LaRC Chief Scientist

  7. Engineering & Technology Management Group Questions on this Session? Exchange: What are the drivers for adopting a knowledge-based approach?  Concerns?  Opportunities? Folder

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