Development of an operational predictive tool Development of an operational predictive tool for visibility degradation and brownout caused for visibility degradation and brownout caused

by rotorcraft dust entrainmentby rotorcraft dust entrainment

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

DEES-DRI, Reno, NVDEES-DRI, Reno, NV

jdmac@dri.edu, DRI 2215 Raggio Pkwy, Reno, NV 89503, DRI 2215 Raggio Pkwy, Reno, NV 89503

IntroductionIntroduction• 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

Experimental dataExperimental data• U.S. Army Yuma Proving

Grounds (May 2007): rotorcraft dust entrainment study

• Variation: speed, height• 70 flight passes, UH-1

Desert Pavement Emission Rate Data

-Dust emissions-Visibility impacts-Helicopter wake structure

Shear stress predictorShear stress predictor

CFDNew EmpiricalMethod

Shear stress predictorShear stress predictor

Wake velocity estimates

Experimental wake structure data

EmpiricalImpingingJet equations

Soils database, dust entrainment modelSoils database, dust entrainment model

m

DpipiipHicij

j

jjiV

i

DpDFefMdF

2000

1,

4

0

)()()()(

DRI Integrated Terrain LandformAnd Soils Database

4 Soil Categories- Dust flux physics Distribution of

Particle sizeSaltation flux- Threshold friction vel.: -Soil moisture

Performance of entrainment modelPerformance of entrainment model

Brownout riskBrownout risk

Example: Full Brown-out, Rating: 10 Example: Full Brown-out, Rating: 8

Brownout riskBrownout risk

Example: Significant Visibility Impacts, Rating 6 (vortex beneath heli)

Example: Significant Visibility Impacts, Rating 7 (vortex in front of heli)

Brownout riskBrownout risk

Example: Moderate Visibility Impacts, Rating 5 Example: Moderate Visibility Impacts, Rating 4

Brownout riskBrownout risk

Example: Minor Visibility Impact, Rating 2

Brownout riskBrownout risk

Brownout risk assessment mappingBrownout risk assessment mapping

Scenario 1:

- Slow speed / landing- Recirculation Pattern- Light winds, neutral

Brownout risk assessment mappingBrownout risk assessment mapping

Scenario 2:

- Moderate speed- Light head wind (left)- Moderate side wind (right)

Brownout risk Brownout risk assessmentassessment mapping mapping

Scenario 3:

- Faster, vortex beneath heli.- Moderate side wind

Scenario 4:

- Very fast, wing-vortex shaped wake- Moderate side wind

ProbabilisticEmission andBrownoutPotential:

• non-linear• ensemble approach• many variables:Environment: - wind speed - wind direction - stability/ turb. - roughness - soil type/dist. - gravel cover - crust coverAircraft: - rotor height - rotor thrust - rotor angle - turb. flux - ground speed - wake structure fluctuation

Tool in-development visual example

ConclusionsConclusions• 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

• 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.

Acknowledgements: