Deriving predictive turbulence data models

Holger Jaenisch; James Handley; Michael Curley; Matthew Edwards; Jai-Ching Wang

doi:10.1117/12.775561

24 March 2008 Deriving predictive turbulence data models

Holger Jaenisch, James Handley, Michael Curley, Matthew Edwards, Jai-Ching Wang

Proceedings Volume 6971, Acquisition, Tracking, Pointing, and Laser Systems Technologies XXII; 69710H (2008) https://doi.org/10.1117/12.775561
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

We present a novel algorithm taking measurements of time, solar irradiance, wind speed, peak wind speed, temperature gradient, and relative humidity to derive a predictive differential equation for mean C_n². Our method derives individual control terms and forcing functions by modeling macro-structure, micro-structure, and fine structure terms independently. The final model is suitable for analysis and able to be used as a baseline expectation model for in situ battlefield use for predictive optical correction or slewing, and possibly for mitigating the effects of wind shear on artillery shells downrange.

Citation Download Citation

Holger Jaenisch, James Handley, Michael Curley, Matthew Edwards, and Jai-Ching Wang "Deriving predictive turbulence data models", Proc. SPIE 6971, Acquisition, Tracking, Pointing, and Laser Systems Technologies XXII, 69710H (24 March 2008); https://doi.org/10.1117/12.775561

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