Tire Wet Traction: Operational Severity and Its Influence on Performance

  • A. G. Veith
  • M. G. Pottinger

Abstract

Tire wet traction, both cornering and braking, depends on several factors. The primary ones are speed, pavement texture, and water depth. The dependence of tire traction on these is complex due to the interaction among these variables. This paper examines some aspects of this complex behavior and offers suggestions on how to overcome part (hopefully a large part) of this complexity.

A concept of Operational Severity is defined. Operational Severity is directly proportional to tire (vehicle) speed and water depth and inversely proportional to tire and pavement water drainage capacities. The logic for this concept is presented.

The difficulties involved in assessing tire wet traction performance at a particular speed are outlined. To overcome this problem, an additional factor, the Ultimate Performance Rating or UPR, is defined. This is basedon the full information content of the linear data plot of wet cornering coefficient, μc vs- speed. Tires rated on the basis of UPR under moderate Operational Severity yield performance values that closely correlate with actual available traction (μc values) under severe Operational Severity.

Data showing the UPR correlation with high operational severity traction are used to illustrate how the actual wet cornering traction coefficient, μc, and relative ratings based upon μc change as water depth varies. This is done for two different pavement textures. Both actual μc and relative ratings are dependent upon water depth at 60 mph. This change in relative rating with varying water depth brings into question the “control tire” concept normally used in this type of testing. This problem is discussed and suggestions made on ways to alleviate some of the difficulties.

Keywords

Water Depth Test Surface Slip Angle Groove Depth Open Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1974

Authors and Affiliations

  • A. G. Veith
    • 1
  • M. G. Pottinger
    • 1
  1. 1.The B. F. Goodrich CompanyBrecksvilleUSA

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