Stochastic Queuing Model Analysis to Support Airspace Super Density Operations (ASDO)

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX11CA08C
Agency Tracking Number: 095451
Amount: $599,994.00
Phase: Phase II
Program: SBIR
Awards Year: 2011
Solicitation Year: 2009
Solicitation Topic Code: A3.01
Solicitation Number: N/A
Small Business Information
Optimal Synthesis, Inc.
CA, Los Altos, CA, 94022-2777
DUNS: 829385509
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Monish Tandale
 Principal Investigator
 (650) 559-8585
 monish@optisyn.com
Business Contact
 Victor Cheng
Title: Business Official
Phone: (650) 650-8585
Email: vcheng@optisyn.com
Research Institution
 Stub
Abstract
NASA has been involved in extensive research efforts to develop advanced concepts and technologies, for the Next Generation Air Transportation System (NextGen) under different Research Focus Areas (RFAs). The Airspace Super Density Operations (ASDO) RFA seeks to develop efficient terminal area operations. It is expected that multiple ASDO concepts will be interacting with one another in a complex non-deterministic manner. Therefore, the overall terminal system performance may not be a straightforward combination of individual performance indices. It is also crucial that the overall system performance be robust to wind and operational uncertainties. The proposed research effort seeks to develop a fast-time, stochastic analysis tool based on queuing theory that can be used to evaluate the interaction and combined performance of multiple ASDO concepts. The utility of the approach was demonstrated under Phase I research.Phase II research seeks to achieve the following: (i) make enhancements to the modeling and simulation aspects of the approach, (ii) accelerate the stochastic simulation execution time using high-performance computing solutions, (iii) create software plug-ins for existing NASA research tools, (iv) conduct studies of NextGen terminal area concepts using the queuing simulation, and (v) develop a conflict free scheduling algorithm based on the queuing simulation.

* information listed above is at the time of submission.

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