The AGDISP Aerial Spray Simulation Model is used to predict the deposition of spray material released from an aircraft. The prediction is based on a well-defined set of input parameter values (e.g., release height, and droplet size) as well as constant data (e.g., aircraft and nozzle type). But, for a given deposition, what are the optimal parameter values? We use the popular Genetic Algorithm to heuristically search for an optimal or near-optimal set of input parameters needed to achieve a certain aerial spray deposition.

In this paper, we describe several genetic algorithm methods to deal with the spray parameter optimization problem, and compare them with the original heuristic method SAGA.

AGDISP (Aerial Spray Simulation Model) is used to predict the deposition of spray material released from an aircraft. Determining the optimal input values to AGDISP in order to produce a desired spray material deposition is extremely difficult (NP hard). SAGA, an intelligent optimization method based on the simple genetic algorithm, was developed to solve this problem. In this paper, we apply several nature inspired heuristics to this problem. The first method still uses the genetic algorithm, but changes its type, selection method, crossover and mutation operator. The second method applies a neural network to improve the initial population, crossover and mutation. The third method uses GADO, a general-purpose approach to solving the parametric design problem. The fourth method applies simulated annealing to this problem. Finally, we compare their performance with SAGA and discuss their applications to the aerial spray deposition problem.

The United States Department of Agriculture — Forest Service (USDA-FS) has been involved in the development of computer models to simulate deposition from aerial pesticide spraying since the early 1970s. Originally, this work was driven by the need to improve the percentage of aerially sprayed material that actually deposited on a target area. The amount of on-target deposition is a primary factor in determining the level of pest control achieved. A second focus of this modeling work that has become the objective in much of the recent work is to use modeling to determine the amount of sprayed material that does not land on the target area and is defined as “drift”. It is assumed that drift causes unintended environmental consequences and is a form of environmental pollution.

The AGDISP Aerial Spray Simulation Model is used to predict the deposition of spray material released from an aircraft. The prediction is based on a well-defined set of input parameter values (e.g., release height. and droplet size) as well as constant data (e.g., air craft and nozzle type). But, for a given deposition, what are the optimal parameter values? We use the popular Genetic Algorithm to heuristically search for an optimal or near-optimal set of input parameters needed to achieve a certain aerial spray deposition. Having this knowledge can benefit forest managers substantially, especially regarding such issues as cost, environmental safety and forest treatment accuracy.

The present effort addresses the technical issues associated with hydrocarbon fuel jettisoning from aircraft, including the adaptation of an existing aerial application model for pesticide deposition. The analysis produces qualitatively and quantitatively reasonable results both in mutlicomponent evaporation calculations for isolated droplets, as well as fully coupled calculationx of airborne fuel jettisoning. Both classes of computations confirm earlier conclusions that the likely groundfall of JP-8 jet fuel is substantially higher than the more volatile JP-4 jet fuel, and reiterate the need for a careful assessment of the environmental impact of fuel jettisoning events involving JP-8. The preliminary model appears to be a suitable starting point for full-scale development of a flexible, practical fuel jettisoning simulations code.

The AGDISP Aerial Spray Simulation Model is used to predict the deposition of spray material released from an aircraft. The prediction is based on a well-defined set of input parameter values (e.g., release height, and droplet size) as well as constant data (e.g., aircraft and nozzle type). But, for a given deposition, what are the optimal parameter values? This problem is considered to be a parametric design problem or more generally a configuration problem. Attempting to optimize a configuration based on some set of constraints is known to be extremely difficult (NP-Hard). We use the popular Genetic Algorithm to heuristically search for an optimal or near-optimal set of input parameters needed to achieve a certain aerial spray deposition. Having this knowledge can benefit forest managers substantially, especially regarding such issues as cost, environmental safety, and forest treatment accuracy.

Abstract : The development of a chemical agent challenge-level model for current and future U.S. Army rotorcraft requires both a cloud dispersion model and a sophisticated helicopter aerodynamic effects model. Since the U.S. Army Research Laboratory (ARL) currently possesses chemical-biological (CB) agent cloud generator and dispersion tracking models, this project focused on modeling the rotor wake and airframe interaction with the CB cloud and the deposition of the CB material on the rotorcraft surfaces. The RotorCRAFT/interactional aerodynamics (RC/IA) code was previously developed under the sponsorship of the U.S. Army Small Business Innovative Research (SBIR) program to provide detailed analysis of steady and unsteady airframe loading attributable to helicopter rotor wake-fuselage interactions. The general aim of the effort outlined here was to tailor the RC/IA code to the modeling requirements of rotor-chemical cloud interaction analysis. The work to date has led to the development of the computer code, Lagrangian Deposition and Trajectory ANalysis/Chemical-Biological (LDTRAN/CB), described in this report. The code is the basis of the Chemical Agent Deposition Analysis for Rotorcraft Surfaces (CADARS) model. The CADARS model is a state-of-the-art chemical agent challenge-level predictive system which can be used to efficiently analyze realistic operational scenarios while capturing physically important rotor wash effects. The particular aircraft that has been the focus of work to date has been the U.S. Army RAH-66 Comanche helicopter, although the modeling methods can be extended to analyze any aircraft in the U.S. Army inventory. The biological agent hazards portion of the CADARS model is presently not included in this version.