DOE site remedial management optimization utilizing on the cloud computing systems - 15676
- HydroGeoLogic, 11107 Sunset Hills Road, Suite 400 Reston, VA 20190 (United States)
Environmental cleanups at complex sites necessarily address many competing demands and answer questions such as: 'What is the best way and in a sustainable manner to minimize risk to human health and the environment during active restoration efforts and/or long term legacy management, while incorporating parameter uncertainty, management considerations, and stakeholder concerns into the process?' Physics Based Management Optimization (PBMO{sup TM}) provides a means to answer such questions. 'Physics Based' indicates incorporation of numerically computed groundwater flow and transport processes into the analysis. This enables optimal remedy design based on comprehensive mass removal/destruction metrics and optimal monitoring strategies. 'Management Optimization' indicates the ability to incorporate objective functions (e.g., management constraints, sustainability considerations) into the evaluation. PBMO{sup TM} components have been successfully implemented for optimal plume delineation (points of compliance) and monitoring, for optimal system design and to optimize existing active treatment systems in the US at multiple DOE, DOD and industrial sites since the mid 1990's. PBMO{sup TM} has been recently extended and implemented on multi-core, multi-CPU grid computing systems including computational clusters, local area networks and the Cloud. This extension allows for increased flexibility and adaptation of computational resources which can now support optimal remedy design and long term management strategies for complex soil and groundwater sites. In the PBMO{sup TM} -Grid case study, we evaluate flow and transport for a typical optimization search for a realistic project against sequential optimization. PBMO{sup TM}-Grid reduces the CPU time required to solve the optimization problem by more than a factor of 14 X compared to sequential optimization. PBMO{sup TM}-Grid can be deployed for deterministic or stochastic optimization and formally computes design risk and predicted degree of remedial action success. The numerical models used can consist of a single 'mega' model (aka a monolith), or a mixture of models and other calculations or expert systems. PBMO{sup T}M-Grid integrates the computational models in a seamless manner, and models run on the Cloud can be implemented in Windows or Linux platforms. (authors)
- Research Organization:
- WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States)
- OSTI ID:
- 22824505
- Report Number(s):
- INIS-US-19-WM-15676; TRN: US19V1072069551
- Resource Relation:
- Conference: WM2015: Annual Waste Management Symposium, Phoenix, AZ (United States), 15-19 Mar 2015; Other Information: Country of input: France; 17 refs.; available online at: http://archive.wmsym.org/2015/index.html
- Country of Publication:
- United States
- Language:
- English
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