2017-280 Abstract

This paper describes the development of a Decision Support Tool (DST) for response planning associated with aerial operations for offshore oil spills. The research program was formulated to include characterization of dispersant spray drift through numerical modeling to generate a database of drift response for a range of airframes and environmental conditions. The drift of aerial dispersants is dependent on a number of different influences including airframe shape and aerodynamics, environmental effects, flight conditions and aerial dispersant make up. As with agricultural spraying, oil spill response spraying has the potential of spray drift to impact upon ecologically sensitive regions and/or areas occupied by people or marine mammals surfacing in the spill area. The development of the DST included an evaluation of existing regulatory models, investigating their application to the offshore environment. It was found that, due to inherent limitations and simplifications, particularly for the larger airframes considered, the existing models were under conservative in comparison with Computational Fluid Dynamics (CFD) models in the near field wake regions for offshore spraying purposes. To address these issues, a combination of scaling factors and the use of inviscid vortex transport and particle dispersion models were adopted for inclusion in the DST. It is envisaged that, once validated further, the DST will become an invaluable tool for Oil Spill Response Operators (OSROs) and decision planners in both the operational mode of providing information to aid in establishing setback distances and in the planning mode to assist with the identification of windows of opportunity conducive to spraying operations.

Introduction

Aerial application of dispersants is a proven means for responding to oil spills both in coastal waters and in the deeper waters of the Outer Continental Shelf.

A number of tools currently exist for aerial spray operational planning, such as the pesticide spray tool AGDISP. These tools have potential applicability for use in oil spill response operations, however they were not developed for use in such scenarios and there is a need to evaluate these existing tools (such as AGDISP) and apply them to the equipment and missions used for oil spill dispersant spraying missions.

In 2015 the Bureau of Safety and Environmental Enforcement (BSEE) instigated a research initiative to develop an aerial dispersant spray drift Decision Support Tool (DST) specifically for application in oil spill response planning. In particular, the tool was to be used for planning aerial dispersant sorties, and assisting in defining safety setback distances to protect people and marine mammals in the spill response area.

As part of the development of the Decision Support Tool, the research program included formulation of numerical Computational Fluid Dynamic (CFD) models of representative oil spill response aircraft. The CFD models facilitated examination of the effects of the combination of environmental conditions likely to be experienced by the aircraft coupled with the specific configuration of the aircraft/dispersal system geometry (such as nozzle configurations). The results from the CFD models were then compared with the existing inventory of dispersion models (i.e. AGDISP) in order to identify conditions in which these existing models are not representative of offshore spill response aircraft. This determined the suitability, or otherwise, of existing models for incorporation into the DST.

The objective of the research program was the production of a decision support software tool which is capable of achieving two key functions:

  1. Determining operability windows for aircraft spray missions based upon the rapid evaluation of forecast and/or measured meteorological conditions over the response area.

  2. Determining the maximum extent of dispersant drift based on environmental conditions at the site. As a minimum, to protect the safety of workers on response vessels in the field, the tool needs to be capable of providing input into the decision for establishing the minimum safety setback distances safe distance from the aerial dispersant operations.

Modes of Operation

The DST features two modes of operation:

  • Operational Mode: The philosophy of the operational mode is to take as input a single wind speed and direction and provide information to aid in the establishment of setback distances.

  • Planning Mode: The philosophy of the planning mode is to take time varying meteorological data to assist with the identification of windows of opportunity conducive to spraying operations based on aerial dispersant spray drift extent.

Overall Framework

The overall framework for the dispersion modeling module of the DST consists of an input module which requires, as a starting point, a selection of the airframe under consideration, the intending spray heading and the meteorology which will require as a minimum the wind speed and direction. The framework has been designed to be flexible, and will allow refinements to be added as future improvements are identified, or new aircraft are to be considered for incorporation, see Figure 1.

Figure 1.

High Level Algorithm For Decision Support Tool

Figure 1.

High Level Algorithm For Decision Support Tool

DST Output Definition

Based upon a use mode and number of inputs the DST tool performs two primary functions:

  • Function 1 – Determine Potential Forecast Windows of Operations (Planning Mode Only)

    • a. Times during which spraying is possible during the forecast period as a table.

    • b. The operability table will list the forecast time period, check whether the limiting conditions are met and indicate whether the forecast period is operable or not-operable.

  • Function 2 – Determine Maximum Extent of Spray Drift

    • c. Output as a map layer (in KML format) for overlay with other operational information.

The predicted extent of dispersant drift distance at the sea surface is set from the 99th percentile horizontal spread of the dispersant particulates on the basis of deposited mass from the aircraft flight path. The selection of the 99th percentile of deposited mass would likely result in a relatively small proportion of the sprayed mass (i.e. 1 percent) still aloft at the boundary. A high level review of the available safety data for the hazardous constituents would indicate that this remaining mass would likely either rapidly dilute below indicated thresholds or will already be below these concentrations at the boundary. However, as toxicity information for some constituents of the dispersants are unpublished, further studies to better characterize the health effects of dispersants may be required. Although the program does not provide concentration results at this time, it is a subject for future development.

DST Operational Parameters

Airframes

The DST is currently configured to predict dispersant drift from four airframes:

  • Air Tractor AT-802A;

  • Lockheed C-130A (Hercules);

  • Douglas DC-3; and

  • Douglas DC-4.

The selection of airframes was made on the basis of aircraft that are or were in use in an operational response role, and the availability of data. The DST has the potential to expand to other available airframes in the future. It should be noted that while the AT-802A participated in the Deepwater Horizon response, these are not currently under contract for response in US waters, however they continue to be contracted in an operational role internationally.

DST Model Development

Basis of Model Selection

The methodology adopted for the model evaluation encompassed a high level capability review of existing aerial dispersion modeling tools to determine which tools have the functionality to model the aerial release of spray dispersant to achieve the required specifications.

A comparison of AGDISP was conducted with higher fidelity Computational Fluid Dynamics (CFD) models, which were developed as part of the project, for a range of cases considered critical to the maximum extent of aerial drift.

A sensitivity study was then conducted of inputs to the numerical models to determine whether differences in the underlying assumptions or input variables of each model can explain any differences identified for the predictions and what set of parameters might be used in the DST to give a conservative estimate of the extent of spray drift.

Using this methodology, the most appropriate method for modeling the aerial dispersant was identified for each of the critical cases. In addition, the set of input parameters most likely to provide a conservative estimate of the spray drift was also identified for use in the DST tool.

High Level Capability Review

In aerial sprays, the majority of the transport of the particles is via advection and gravitation. As such, there is a need to quantify specific features within the fluid flow to account for the various forces acting on the individual particles. Accurately modeling the influence of the aircraft can only be achieved through quantification of these flow features. Qualification of these flow features maybe achieved through experimentation (which may be expensive and hard to replicate, CFD, or through empirical and first principle methods).

The primary tools that have regulatory recognition for the dispersion of spray behind aircraft in the US fall within the third category of empirical/first-principle models. While CFD is increasingly recognized as a potential tool for use in exploring complex dispersion scenarios, it is currently limited in its applicability to a case-by-case basis. As such, the CFD undertaken in this study using Star-CCM+ (CD-Adapco, 2015) was used to evaluate the predictions from existing models or were used as an input to far-field dispersion in another modeling tool.

A number of existing models were assessed including AGDISP, AgDRIFT, Airmod, ISC3 and Calpuff. Based upon the capability assessment, for the particular application as the computation core of the DST, the ideal candidate was determined to be AGDISP due to its ability to estimate the aerodynamic forcing caused by the presence of the aircraft. In addition, AGDISP has the benefit of being able to incorporate the influence of atmospheric stability into the modeling results, addressing a key limitation of CFD modeling for extending the results of the simulation to all potential environmental conditions at distances beyond the immediate wake region.

AGDISP Evaluation For DST Application

A more detailed evaluation assessment was conducted to determine if AGDISP is applicable to the offshore context. Recently, there has been at least one paper that has identified potential issues in AGDISP’s representation of the wake under certain extreme crosswind conditions (Ryan, Gerber, & Holloway, 2013).

AGDISP Simplifications

There are known limitations and simplifications present in AGDISP wake modeling that were evaluated and assessed. To decrease computational time, AGDISP limits which aircraft wake mechanisms are computed, and how these in turn influence the particles. The factors affecting the wake modeled by AGDISP are as follows (Bird et al., 2002):

  • Downwash from the wing: the downwash caused by the lift generated by the wing is estimated using lifting line theory. The downwash is assumed to be uniformly distributed and pointed downward at the trailing edge of the wing.

  • Wingtip vortices: similarly estimated using an assumption related to the lift distribution of the wing using lifting line theory. This approach assumes an elliptical lift distribution across the wing. The vortex decay rate has been estimated for all airframes to be based on a value established from aircraft flyovers.

  • Propellers: the influence of propellers is computed using actuator disc theory. In order to establish the required thrust, all aircraft have an assumed drag coefficient of 0.1, which is typically a conservative value.

  • Crosswind: the influence of crosswind is computed on the basis of the vertical velocity gradient predicted by an atmospheric boundary layer approach.

  • No body effects: the influence of the body creating regions of low pressure behind the aircraft, potentially influencing the resultant particle dispersion have been neglected.

  • No flap or tail vortices: the generation of vortices as a result of the presence of flaps or the tail have not been quantified and are neglected. As such, the effect of these vortices on the spray drift have not been considered.

It is noted that this does not include all potential sources of wake turbulence generation, however results from field trials (Duan, Yendol, Mierzejewski, & Reardon, 1992) indicated that these mechanisms appear sufficient for the agricultural aircraft AGDISP was designed to model.

The AGDISP modeling package was designed for use in optimizing pesticide spraying operations employing agricultural aircraft. A significant body of validation data has been built surrounding the use of AGDISP, though this has been largely driven by the Spray Drift Task Force using agricultural aircraft. Many of the studies referenced in the development (Milton E. Teske et al., 2002) and the validation (Duan et al., 1992) of AGDISP cite the use of aircraft such as Air Tractors or variants of the Cessna 188.

While the Air Tractor was used during the Deepwater Horizon oil spill response, no agricultural aircraft are under contract for dispersant spraying by United States Coast Guard certified dispersant OSROs. As such, aircraft employed in any future offshore oil spill response context are likely to be a larger, multi-engine aircraft with significantly larger tank capacities. Furthermore, the offshore context requires a large quantity of dispersant to be released as rapidly as possible (US Coast Guard, 2013), with dispersants applied in dosages measured in gallons per acre, where pesticides are typically applied at rates of oz per acre. It is this difference in operational context and aircraft types that was evaluated in the current study for potential impact on spray drift.

AGDISP Comparison with CFD

To determine if AGDISP was suitable for use in the offshore context due to the variation in aircraft types, this assessment was done for each aircraft. The predicted fraction aloft from AGDISP was compared to the results from CFD simulations for similar spraying conditions. This comparison was made for each airframe by matching the operating scenarios modeled in the CFD models in AGDISP and entering them in AGDISP.

The size distribution for dispersant droplet size used in this study has a Volume Mean Diameter (VMD) of 341 microns. Sensitivity analyses considering the ASAE fine to medium and coarse to very coarse size distributions indicated that droplet size distribution has a significant impact on the maximum extent of spray drift, with larger particles depositing earlier. As a result, conducting spraying operations with spray equipment which results in a larger VMD droplet size, and fewer small particles, will result in less drift.

1) Air Tractor AT-802A

A large number of comparison cases were studied for varying wind speed, aircraft speed and altitude (the maximum altitude assessed was 50 ft as this was the upper limit reported in the Dispersant Mission Planner (DMP2) software for the aircraft), Figure 2 shows an example comparison of the mass fraction aloft for the crosswind case of 20 knots at an altitude of 16 ft. This demonstrates close correlation between the CFD and AGDISP predictions in the near field region, beyond which AGDISP provides a more conservative prediction of the mass aloft as a function of down wind distance, comparisons made at different aircraft speeds and altitudes showed similar correlation.

Figure 2.

AT-802A Comparison of AGDISP and CFD Results

Figure 2.

AT-802A Comparison of AGDISP and CFD Results

In general, there is good agreement between the predicted fraction aloft using the AGDISP and CFD models of the AT-802A for prediction of spray drift perpendicular to the aircraft track. AGDISP was found to be more conservative in a majority of crosswind conditions, and when there is a component of the wind perpendicular to the track of the aircraft (intermediate wind angle cases), see Figure 2.

2) Lockheed C-130A Hercules

Figure 3 shows a comparison of the mass fraction aloft for the crosswind of 20 knots at an altitude of 75 ft. These results show that there is a noticeable difference between the predicted particle transport behavior using AGDISP and CFD. In the near field region, the CFD simulations appear more conservative, conversely in the far field (beyond 700 ft) AGDISP provides a conservative estimate of the extent of spray drift.

Figure 3.

C-130A Comparison of AGDISP and CFD Results

Figure 3.

C-130A Comparison of AGDISP and CFD Results

The flow field behind the C-130 was analyzed to determine the cause of the difference in predictions. Figure 4 shows the flow field behind the C-130A, wherein the gray iso-surface indicates the presence of vortices while the streamlines show the trajectory of the particles. This image clearly shows the particles released from the spray boom being drawn up behind the C-130A, an effect that can be observed in photographs of the C-130 conducting spray operations. The effect occurs in a region where vortices are not present indicating that this is likely due to the tapered body shape of the rear of the C-130A.

Figure 4.

C-130A Visualization of Particle Tracks in Near Field Body Effect

Figure 4.

C-130A Visualization of Particle Tracks in Near Field Body Effect

Given the presence of the body effect and the general poor far field correlation between AGDISP and CFD described in Figure 3, it was concluded that AGDISP cannot be used without modification to predict the maximum extent of spray drift for the C-130A, however there was scope to scale the results to address the differences identified.

3) Douglas DC-3

Figure 5 shows a comparison of the mass fraction aloft for the crosswind of 20 knots at an altitude of 75 ft. These results show that there is a significant difference between the predicted particle transport behavior using AGDISP and CFD in which the CFD results are always conservative.

Figure 5.

DC-3 Comparison of AGDISP and CFD Results

Figure 5.

DC-3 Comparison of AGDISP and CFD Results

The flow field behind the DC-3 was analyzed to determine the cause of the observed difference. Figure 6 shows the flow field immediately behind the DC-3. The gray iso-surface shows the presence of vortices while the streamlines show the trajectory of the particles. This image clearly shows that the spray boom extends into the wing tip vortex region that causes a large number of particles to be entrained in the wing-tip vortex. Extending the boom beyond 65% to 70 % of the wing semi-span is not recommended practice (Barbosa, 2010; Teske, Thistle, Barry, & Eav, 1998).

Figure 6.

DC-3 Visualization of Particles Released in Wing Tip and Flap Vortices

Figure 6.

DC-3 Visualization of Particles Released in Wing Tip and Flap Vortices

In addition to the wing-tip vortex Figure 6 shows a strong vortex being shed off the edge of the deployed flap. Given the limited information provided by the OSROs with regard to their spray operating conditions, the exact amount of flap deployment during dispersant spraying operations was not directly known. Analysis accounting for the regulatory speeds for spray dispersal from the DC-3 shows that flap deployment is required to maintain sufficient lift. The presence of the flap vortex affects the spray trajectory in both the near and far field. Figure 7 shows that in the far field the wing-tip and flap vortices interact in a co-rotating vortex pair. This co-rotating vortex pair process around each other so that the inner flap vortex travels down beneath the wing tip vortex before being pulled upward, lifting the particles entrained in the flap vortex further off the ground and keeping them aloft longer.

Figure 7.

DC-3 Visualization of Particles in Flap-Wingtip Vortex Interaction

Figure 7.

DC-3 Visualization of Particles in Flap-Wingtip Vortex Interaction

As AGDISP is based on lifting line theory it does not account for multiple vortex interactions and, as such, is not capable of capturing this behavior. Given the presence of the flap vortex pair and the general poor correlation between AGDISP and CFD described in Figure 5, AGDISP cannot be used to predict the maximum extent of spray drift for the DC-3.

4) Douglas DC-4

For the Douglas DC-4, comparison of the predicted aerial spray dispersion patterns from AGDISP and CFD was not possible. The DC-4 uses a unique spray boom position which lies above the trailing edge of the wing. AGDISP restricts spray configurations to those with an under wing spray arrangement and thus it was not possible to model the Douglas DC-4 in AGDISP.

Despite the limitations of AGDISP, the aerial spray pattern behind the DC-4 was modeled using CFD. Figure 8 shows the interaction of flow particles (streamlines) with the vortices shed from the wing tips, wing flaps and tail. Like the Douglas DC-3 the flow behind the DC-4 is affected by the presence of the flap vortex. In this case, the flap vortex and wing tip vortex merge to form a single vortex structure downstream of the aircraft. The interaction of these complex flow structures affects the dispersion of particles in the near field; care must be taken to consider this interaction when modeling the far field drift of aerial dispersant.

Figure 8.

DC-4 Visualization of Particles in Flap-Wingtip Vortex Interaction

Figure 8.

DC-4 Visualization of Particles in Flap-Wingtip Vortex Interaction

AGDISP Evaluation Summary Findings

The key outcomes of the evaluation conducted of the AGDISP modeling system for use in offshore spill response are summarized below:

  • Air Tractor AT-802A: For the purposes of offshore spill response, the AT-802A is well represented by AGDISP in that no significant modifications to the results are required.

  • Lockheed C-130A: It has been identified that, due to the wake effect caused by the shape of the fuselage in the vicinity of the rear cargo door, the results from AGDISP are not representative in the near field (10–50 chord lengths), in that the fuselage wake delays the deposition of the spray. However, in the far field, AGDISP predicts a lower deposition rate than the CFD model, and as such AGDISP results may be modified to provide a conservative estimate of the extent of spray drift.

  • Douglas DC-3: Due to the presence of vortices generated at the edge of the main wing flaps, and the spray boom extending sufficiently along the wing semi-span to inject particles into the tip vortices, the DC-3 is poorly represented by AGDISP, and an alternative approach is required to determine the drift impacted area.

  • Douglas DC-4: The DC-4 has a spray boom which lies above the trailing edge of the wing. AGDISP will not allow spray nozzle positions above the trailing edge of the wing, as such, the DC-4 cannot be modeled using AGDISP. While the presence of flap vortices also appears to influence the DC-4 spray, the closer proximity of the flaps to the wing tips causes the two vortices to merge earlier than those generated by the DC-3, reducing the influence of the flap vortices.

Assessing Aerial Dispersant Using DST
Spray Drift Nomenclature

The spray drift reported in the DST is reported as spanwise drift, trackwise drift and total drift distance. Figure 9 describes the definition of each of these drift directions. Trackwise drift is the component of drift behind the aircraft along the track of the aircraft. Spanwise drift is the component of drift perpendicular to the track of the aircraft. Total drift distance is the absolute drift distance, this distance is normally aligned with the wind direction.

Figure 9.

Drift Direction Definitions

Figure 9.

Drift Direction Definitions

Degree of Uncertainty

As the CFD had identified that not all of the airframes may be represented directly by AGDISP, the approach for computing the maximum spray drift will rely on the use of the AGDISP model where it has been determined that AGDISP is representative. Where it is not representative, an alternative approach is proposed.

AGDISP Representative of Spray Drift

For those airframes which have been identified as being representatively modeled using AGDISP, the following algorithm has been proposed. For the current study, only the Air Tractor AT-802A falls into this category.

The approach is based on a combination of the following factors:

  1. Application of a computation of intermediate angles using sine and cosine components of the maximum crosswind extent. The change in spray drift extent for intermediate wind angles were investigated and in effect, it was identified that these could be predicted by computing the lateral and axial components using the wind angle.

  2. Based upon comparisons between CFD and AGDISP as well as comparisons between the crosswind extent and the headwind extent, it was identified that AGDISP predicted a slightly lower extent by approximately 10% to 15%. As such, a proposed 15% calibration factor is used.

  3. Minimum drift extent associated with a 4 kn wind for a particular aircraft height and velocity which is considered a conservative minimum based on the expected ground effect of a vortex.

AGDISP Partially Representative of Spray Drift

For those airframes which have been identified as being partially representatively modeled using AGDISP, the algorithm detailed in the AGDISP Representation section has been used. For the current study, only the Lockheed C-130A falls into this category.

The approach is based on the determination of the following factors:

  1. Given the deficiency in AGDISP to model both multi vortex systems and the body effect an additional uplift extent-scaling (or correlation) factor will be applied to the AGDISP predictions to allow for any fuselage effect.

  2. Based upon comparisons between CFD and AGDISP it was identified that AGDISP incorrectly predicted the near fields but provided a conservative prediction in the far field. Therefore the maximum error in prediction in the near field was used as a calibration factor to account for this uncertainty and was applied prior to computing the downwind and crosswind components, in addition to the 15% uplift extent-scaling (or correlation) factor as indication in the AGDISP Representative of Spray Drift Section.

AGDISP Not Representative of Spray Drift

For the cases where AGDISP was found to be non-conservative an independent inviscid vortex transport model was used. Unlike AGDISP, this model was seeded using a flow field extracted from the CFD models and therefore could include both wing- and flap-tip vortices. The position of the particles can also be modeled at any point relative to the vortices, such that it is capable of modeling the above wing spray boom configuration for the DC-4.

This method was found to depict good correlation with the CFD in that it follows the trends of both the standard AGDISP implementation and the CFD in the far field, as shown in Figure 10.

Figure 10.

Inviscid Vortex Solution For DC-3

Figure 10.

Inviscid Vortex Solution For DC-3

This approach is used to generate data for the look up tables used in the DST for the DC-3 and DC-4 airframes.

DST Sample Results

Operational Mode

For the DST, two modes have been implemented which the user selects from an initial dialog box. The Operational Mode has been provided to allow an estimate of the maximum drift for a given set of meteorological conditions, (i.e. the daily marine forecast) as shown in Figure 11. In Operational Mode, all inputs are repeated in a text output screen to assist in confirming the basis for a given prediction.

Figure 11.

Output from the DST in Operational Mode.

Figure 11.

Output from the DST in Operational Mode.

Based on the available aircraft and the daily forecast, a single estimated maximum extent (in this example, 4750ft at a heading of 173 degrees from the edge of the spray area) can be computed. This prediction can be used to inform decisions on setback distances, particularly if a user-defined Safety Factor is set, allowing this distance to be computed directly.

Planning Mode

In Planning Mode, the tool takes a time varying series of input forecast environmental conditions, upon which it will make a determination of operability windows, that is to say periods of time where the combined meteorological conditions are conducive to spray operations. Furthermore, any predictions of forecast oil spill area can be input (as a Geographic Information Systems (GIS) polygon, currently as a .kml format). The user inputs the area where it is intended to potentially allow spray operations to occur (also as a GIS polygon). Altering the potential spray area would allow Planning personnel to consider different future spray scenarios, and inform forward planning of spray operations as new forecast predictions became available.

Based on engagement with personnel involved in oil spill response, a set of operability criteria for spray operations was established, as previously described. As aerial spray operations are limited to daylight hours (Visual Flight Rules conditions), the DST estimates local sunrise and sunset based on the latitude associated with the input polygons. Evaluating the weather forecast for against the operability produces a table indicating which forward periods will potentially allow spraying to occur, an example of which is shown in Figure 12.

Figure 12.

Example Output Operability Table from the DST

Figure 12.

Example Output Operability Table from the DST

Further to the outputs of the operability table, a polygon is output from the DST indicating the area impacted by spray drift. For an example scenario, a DC-3 spraying at an altitude of 100 ft flying at a heading of N, subject to an initial 30 kn wind Northerly wind, followed by a light 2.2 kn wind condition at 20° from True North. The input polygons, Figure 13, are intersected, and the resultant polygon is stretched by the predictions made by the DST, Figure 14, aggregating the predictions based on the changing wind patterns.

Figure 13.

Example Input Polygons for Forecast Oil Spill Region (black) and Potential Spray Operations Area (Red).

Figure 13.

Example Input Polygons for Forecast Oil Spill Region (black) and Potential Spray Operations Area (Red).

Figure 14.

Example Output Polygon for Spray Impact Area (Yellow) from the DST

Figure 14.

Example Output Polygon for Spray Impact Area (Yellow) from the DST

Combining the estimates for subsequent periods into a single polygon will allow the entire area estimated to be impacted by spray drift for a given forecast period to be reviewed within a GIS package. These polygons can be imported as a further map layer to assist with understanding whether aerial operations will potentially impact on response vessels, environmental areas or other sensitive receptors.

Conclusions

This paper has outlined the development of a Decision Support Tool (DST) for response planning associated with aerial operations for offshore oil spills. The intent of the tool is to predict the extent of spray drift using information extracted from a built in database either as a lookup table or a response surface model/shape functions. This tool allows the rapid screening assessment of a combination of meteorological and oil dispersion data to identify potential windows for aircraft spray operations. The tool will also facilitate establishment of the extent of the dispersant drift such that safety setback distances may be specified for vessels in the affected area.

The methodology adopted for the DST development involved the identification of the most suitable model from the existing atmospheric dispersion tools that would be used as the computation core of the DST. The selected candidate was established to be AGDISP due to its ability to estimate the aerodynamic forcing caused by the presence of aircraft. Four different airframes were investigated to assess the prediction of dispersant drift under the environmental conditions and operational limits.

As limited experimental and field trial data was available during the development of the DST, CFD was used. Validation of the DST outputs against field trial data should be conducted during the BETA testing phase of the DST.

By resolving the items noted above, the DST is capable of providing oil spill responders information related to the extent of areas potentially impacted by dispersant drift. As a proven tool, this can assist operational control personnel in establishing safe setback distances, as well as facilitating de-conflicting response operations.

It is envisaged that the DST will become an invaluable tool for OSROs and decision planners in both the operational mode, providing information to aid in establishing setback distances, and in the planning mode, to assist with the identification of windows of opportunity conducive to spraying operations. There are a number of potential future enhancements to the DST which could include, determining operational spray areas based on identifying areas where overspray is not permitted, and providing outputs of predicted ground level concentrations of dispersant as a contour feature.

Acknowledgments

The authors would wish to acknowledge the financial support and constructive direction provided by BSEE. The authors would also like to acknowledge Peter Kriznic, Steven Wong and Genevieve Beck for their contributions to the study.

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