Dr. Daniel Grahn Profile

Dr. Daniel Grahn

Principal AI Scientist at Altamira Technologies Corp

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Publications (2)

Proceedings Article | 6 June 2022 Poster + Paper

The prediction management framework: ethical, governable, and interpretable deployment of artificial intelligence/machine learning systems

Daniel Grahn, Melonie Richey

Proceedings Volume 12113, 1211327 (2022) https://doi.org/10.1117/12.2617772

KEYWORDS: Artificial intelligence, Data modeling, Safety, Systems modeling, Performance modeling, Machine learning, Defense and security, Process modeling, Standards development, Human-machine interfaces

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Proceedings Article | 7 May 2012 Paper

Potential impacts of elevated aerosol layers on high energy laser aerial defense engagements

Steven Fiorino, Stephen Shirey, Michelle Via, Daniel Grahn, Matthew Krizo

Proceedings Volume 8380, 83800T (2012) https://doi.org/10.1117/12.919173

KEYWORDS: Aerosols, Atmospheric modeling, Climatology, Humidity, Atmospheric particles, Optical turbulence, Defense and security, Laser energy, Absorption, Scattering

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This study quantifies the impacts on high energy laser (HEL) air defense performance due to atmospheric effects in the marine boundary layer driven by varying elevated aerosol layers. The simulations are run using several different engagement geometries to more completely show the effects of aerosols. High adaptive optics are applied to reduce the turbulence effects. The atmospheric effects are defined using the worldwide probabilistic climatic database available in the High Energy Laser End-to-End Operational Simulation (HELEEOS) model. The anticipated effects on HEL propagation performance is assessed at 1.0642 μm across the world's oceans, mapped on a 1° × 1° grid, and at 573 land sites. The scenarios evaluated are primarily near-surface and horizontal over ranges up to 10000 meters. Seasonal and boundary layer variations (summer and winter) for a range of relative humidity percentile conditions are considered. In addition to realistic vertical profiles of molecular and aerosol absorption and scattering, correlated optical turbulence profiles in probabilistic (percentile) format are used. Results indicate profound effects of elevated aerosol layers on HEL engagements as compared to standard scenarios without elevated layers. Also, results suggest changing optical properties to have additional significant effects. HELEEOS includes a fast-calculating, first principles, worldwide surface to 100 km, atmospheric propagation and characterization package. This package enables the creation of profiles of temperature, pressure, water vapor content, optical turbulence, atmospheric particulates and hydrometeors as they relate to line-by-line layer transmission, path and background radiance at wavelengths from the ultraviolet to radio frequencies. Physics-based cloud and precipitation characterizations are coupled with a probability of cloud free line of sight (CFLOS) algorithm for air-to-air, air-tosurface, and surface-to-air (or space) look angles. HELEEOS characterizes aerosol environments using the Advanced Navy Aerosol Model (ANAM) or various representations of maritime particulates from the Global Aerosol Dataset (GADS). In the lowest 50 m, HELEEOS defines optical turbulence with the Navy Surface Layer Optical Turbulence (NSLOT) model. HELEEOS was developed under the sponsorship of the High Energy Laser Joint Technology Office.

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