The Design and Analysis for the Icing Wind Tunnel Experiment of a New Deicing Coating

Xiaodong Li, Xu He, Yuanzhen He, Hui Zhang, Zhong Zhang, Dennis K.J. Lin

Research output: Contribution to journalArticlepeer-review

Abstract

A new kind of deicing coating is developed to provide aircraft with efficient and durable protection from icing-induced dangers. The icing wind tunnel experiment is indispensable in confirming the usefulness of a deicing coating. Due to the high cost of each batch relative to the available budget, an efficient design of the icing wind tunnel experiment is crucial. The challenges in designing this experiment are multi-fold. It involves between-block factors and within-block factors, incomplete blocking with random effects, related factors, hard-to-change factors, and nuisance factors. Traditional designs and theories cannot be directly applied. To overcome these challenges, we propose using a step-by-step design strategy that includes applying a cross array structure for between-block factors and within-block factors, a group of balanced conditions for optimizing incomplete blocking, a run order method to achieve the minimum number of level changes for hard-to-change factors, and a zero aliased matrix for the nuisance factors. New (theoretical) results for D-optimal design of incomplete blocking experiments with random block effects and minimum number of level changes are obtained. Results of the experiments show that this novel deicing coating is promising in offering both high efficiency of ice reduction and a long service lifetime. The methodology proposed here is generalizable to other applications that involve nonstandard design problems. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1417-1429
Number of pages13
JournalJournal of the American Statistical Association
Volume112
Issue number520
DOIs
StatePublished - Oct 2 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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