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Risk Management for the Tiles of the Space Shuttle

Author

Listed:
  • M.-Elisabeth Paté-Cornell

    (Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305)

  • Paul S. Fischbeck

    (Department of Engineering and Public Policy and Department of Decision and Social Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

The tiles of the space shuttle orbiter are critical to its safety at reentry, and their maintenance between flights is time-consuming. We performed a probabilistic risk analysis to identify the most risk-critical tiles and to set priorities in the management of the heat shield. The model is based on a multiple partition of the orbiter's surface. For the tiles in each zone, we used the following data: (1) the probability of debonding due either to debris hits or to a poor bond, (2) the probability of losing adjacent tiles once the first one is lost, (3) the probability of burn-through given the final size of the failure patch, and (4) the probability of failure of a critical subsystem under the skin of the orbiter if a burn-through occurs. A risk-criticality scale was designed based on the results of this model. It is currently used (along with temperature charts) to set priorities for the maintenance of the tiles. We found that 15 percent of the tiles account for about 85 percent of the risk and that some of the most critical tiles are not in the hottest areas of the orbiter's surface. We recommended that NASA inspect the bond of the most risk-critical tiles and reinforce the insulation of the external systems (external tank and solid rocket boosters) that could damage the high-risk tiles if it debonds at take-off. We computed that such improvements of the maintenance procedures could reduce the probability of shuttle accident attributable to tile failure by about 70 percent.

Suggested Citation

  • M.-Elisabeth Paté-Cornell & Paul S. Fischbeck, 1994. "Risk Management for the Tiles of the Space Shuttle," Interfaces, INFORMS, vol. 24(1), pages 64-86, February.
    • Handle: RePEc:inm:orinte:v:24:y:1994:i:1:p:64-86
    DOI: 10.1287/inte.24.1.64
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    Citations

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    Cited by:

    1. Tianyang Wang & James S. Dyer & John C. Butler, 2016. "Modeling Correlated Discrete Uncertainties in Event Trees with Copulas," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 396-410, February.
    2. Elisabeth Paté‐Cornell, 2002. "Risk and Uncertainty Analysis in Government Safety Decisions," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 633-646, June.
    3. Donald L. Keefer & Craig W. Kirkwood & James L. Corner, 2004. "Perspective on Decision Analysis Applications, 1990–2001," Decision Analysis, INFORMS, vol. 1(1), pages 4-22, March.
    4. M. Elisabeth Paté-Cornell & Robin L. Dillon, 2006. "The Respective Roles of Risk and Decision Analyses in Decision Support," Decision Analysis, INFORMS, vol. 3(4), pages 220-232, December.
    5. M.‐Elisabeth Paté‐Cornell & Peter J. Regan, 1998. "Dynamic Risk Management Systems: Hybrid Architecture and Offshore Platform Illustration," Risk Analysis, John Wiley & Sons, vol. 18(4), pages 485-496, August.
    6. Qiuxiang Jiang & Tian Wang & Zilong Wang & Qiang Fu & Zhimei Zhou & Youzhu Zhao & Yujie Dong, 2018. "HHM- and RFRM-Based Water Resource System Risk Identification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 4045-4061, September.
    7. Runlin Zhang & Nuo Xu & Kai Zhang & Lei Wang & Gui Lu, 2023. "A Parametric Physics-Informed Deep Learning Method for Probabilistic Design of Thermal Protection Systems," Energies, MDPI, vol. 16(9), pages 1-20, April.

    More about this item

    Keywords

    space program; reliability: failure models;

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