J.D. McAlpine*, Darko Koracin: DAS-DRI, Reno, NV Steven Bacon, Sophie Baker, Eric McDonald:

1. Development of an operational predictive tool for visibility degradation and brownout caused by rotorcraft dust entrainment J.D. McAlpine*, Darko Koracin: DAS-DRI, Reno, NV Steven Bacon, Sophie Baker, Eric McDonald: DEES-DRI, Reno, NV jdmac@dri.edu, DRI 2215 Raggio Pkwy, Reno, NV 89503

2. Introduction • Brownout problem • Modeling Categories: - Pilot-in-loop & Simulation - Air Quality Purposes - Risk Assessment & Planning • Development of an efficient predictive tool: 1) Wake u* 2) Landform Soil 3) Dust Entrain. 4) Visibility Risk 5) Downwind Database Model Tool Dispersion Tool

3. Experimental data • U.S. Army Yuma Proving Grounds (May 2007): rotorcraft dust entrainment study • Variation: speed, height • 70 flight passes, UH-1 • Dust emissions • Visibility impacts • Helicopter wake structure Desert Pavement Emission Rate Data

4. Shear stress predictor New Empirical Method CFD

5. Shear stress predictor Experimental wake structure data Wake velocity estimates Empirical Impinging Jet equations

6. Soils database, dust entrainment model 4 Soil Categories - Dust flux physics DRI Integrated Terrain Landform And Soils Database Distribution of Particle size • Saltation flux • - Threshold friction vel.: • Soil moisture

7. Performance of entrainment model

8. Brownout risk Example: Full Brown-out, Rating: 10 Example: Full Brown-out, Rating: 8

9. Brownout risk Example: Significant Visibility Impacts, Rating 6 (vortex beneath heli) Example: Significant Visibility Impacts, Rating 7 (vortex in front of heli)

10. Brownout risk Example: Moderate Visibility Impacts, Rating 5 Example: Moderate Visibility Impacts, Rating 4

11. Brownout risk Example: Minor Visibility Impact, Rating 2

12. Brownout risk

13. Brownout risk assessment mapping • Scenario 1: • Slow speed / landing • Recirculation Pattern • Light winds, neutral

14. Brownout risk assessment mapping • Scenario 2: • Moderate speed • Light head wind (left) • Moderate side wind (right)

15. Brownout risk assessment mapping • Scenario 3: • Faster, vortex beneath heli. • Moderate side wind • Scenario 4: • Very fast, wing-vortex shaped wake • Moderate side wind

16. Probabilistic • Emission and • Brownout • Potential: • non-linear • ensemble approach • many variables: • Environment: • - wind speed • - wind direction • - stability/ turb. • - roughness • - soil type/dist. • - gravel cover • - crust cover • Aircraft: • - rotor height • - rotor thrust • - rotor angle • - turb. flux • - ground speed • - wake structure • fluctuation Tool in-development visual example

17. Conclusions • Efficient brownout/ dust-entrainment tool in development for the purposes of: • - risk assessment • - air quality / visibility impacts • - local-scale planning • Empirical helicopter wake model: • - prediction of shear stress field • - shown to adequately produce • State-of-the-art dust entrainment model • - emission rates compare well to experimental data • - adequately predicts brownout/ vis. Impact potential • - ensemble method produces a probability distribution • of risk • Applications presented: • - brownout risk mapping • - dispersion tool for operation planning

18. Acknowledgements: • This material is based upon work supported by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract number DAAD 19-03-1-0159. This work is part of the DRI Integrated Desert Terrain Forecasting for Military Operations Project. • We would also like to acknowledge the contributions from the Strategic Environmental Research and Development Program (SERDP), Sustainable Infrastructure Project SI-1399, of logistical support in the field to J.D. McAlpine and the dust concentration data used in our analysis. • The team would also like to express their gratitude to the Natural Environments Test Office, Yuma Proving Ground, Yuma AZ for financial and logistical support of the helicopter flights.