|Developer(s)||GoldSim Technology Group LLC|
12.0 / February 15, 2017
GoldSim is dynamic, probabilistic simulation software developed by GoldSim Technology Group. This general-purpose simulator is a hybrid of several simulation approaches, combining an extension of system dynamics with some aspects of discrete event simulation, and embedding the dynamic simulation engine within a Monte Carlo simulation framework.
While it is a general-purpose simulator, GoldSim has been most extensively used for environmental and engineering risk analysis, with applications in the areas of water resource management     , mining   , radioactive waste management     , geological carbon sequestration , aerospace mission risk analysis  and energy.
In 1990, Golder Associates, an international engineering consulting firm, was asked by the United States Department of Energy (DOE) to develop probabilistic simulation software that could be used to help with decision support and management within the Office of Civilian Radioactive Waste Management. The results of this effort were two DOS-based programs (RIP and STRIP), which were used to support radioactive waste management projects within the DOE.
In 1996, in an effort funded by Golder Associates, the US DOE, the Japan Nuclear Cycle Development Institute (currently the Japan Atomic Energy Agency) and the Spanish National Radioactive Waste Company (ENRESA), the capabilities of RIP and STRIP were incorporated into a general purpose Windows-based simulator called GoldSim. Subsequent funding was also provided by NASA.
Initially only offered to the original funding organizations, GoldSim was released to the public in 2002. In 2004, GoldSim Technology Group LLC was spun off from Golder Associates and is now a wholly independent company.
Notable applications include providing the simulation framework for: 1) the Yucca Mountain Repository Performance Assessment model developed by Sandia National Laboratories; 2) a comprehensive system-level computational model for performance assessment of geological sequestration of CO2 developed by Los Alamos National Laboratory; 3) a flood operations model to help better understand and fine tune operations of a large dam used for water supply and flood control in Queensland, Australia; and 4) models for simulating risks associated with future manned space missions in NASA’s Constellation program developed by NASA Ames Research Center.
GoldSim provides a visual and hierarchical modeling environment, which allows users to construct models by adding “elements” (model objects) that represent data, equations, processes or events, and linking them together into graphical representations that resemble influence diagrams. Influence arrows are automatically drawn as elements are referenced by other elements. Complex systems can be translated into hierarchical GoldSim models by creating layer of “containers” (or sub-models). Visual representations and hierarchical structures help users to build very large, complex models that can still be explained to interested stakeholders (e.g., government regulators, elected officials, and the public).
Though it is primarily a continuous simulator, GoldSim has a number of features typically associated with discrete simulators. By combining these two simulation methods, systems that are best represented using both continuous and discrete dynamics can often be more accurately simulated. Examples include tracking the quantity of water in a reservoir that is subject to both continuous inflows and outflows, as well as sudden storm events; and tracking the quantity of fuel in a space vehicle as it is subjected to random perturbations (e.g., component failures, extreme environmental conditions).
Because the software was originally developed for complex environmental applications, in which many inputs are uncertain and/or stochastic, in addition to being a dynamic simulator, GoldSim is a Monte Carlo simulator, such that inputs can be defined as distributions and the entire system simulated a large number of times to provide probabilistic outputs. As such, the software incorporates a number of computational features to facilitate probabilistic simulation of complex systems, including tools for generating and correlating stochastic time series, advanced sampling capabilities (including latin hypercube sampling, nested Monte Carlo analysis, and importance sampling), and support for distributed processing.
- Alfred Kalyanapu, Jason Lillywhite, Brantley Thames and Ebrahim Ahmadisharaf (2014),Probabilistic Analysis To Evaluate The Effects Of Dam Breach Methodologies On Downstream Flood Hazard, Proceedings of the World Environmental & Water Resources Congress 2014, Portland, Oregon.
- Erfan Goharian and Steven J. Burian (2014), Integrated Urban Water Resources Modeling In A Semi-Arid Mountainous Region Using A Cyberinfrastructure Framework, Proceedings of the 11th International Conference on Hydroinformatics, HIC 2014, New York, New York.
- Eset Alemu, Richard Palmer, Austin Polebitski and Bruce Meaker (2011), Decision Support System for Optimizing Reservoir Operations Using Ensemble Streamflow Predictions, Journal of Water Resources Planning and Management, 137(1), 72–82.
- Michel Raymond (2014), Wivenhoe Somerset Dam Optimisation Study – Simulating Dam Operations for Numerous Floods, Proceedings of Australian National Committee on Large Dams (ANCOLD) Annual Conference 2014, Canberra, Australia.
- Luke Toombes and Rob Ayre (2014), Holistic Dam Operations Assessment for Southeast Queensland, Proceedings of Australian National Committee on Large Dams (ANCOLD) Annual Conference 2014, Canberra, Australia.
- Brent Usher, Roald Strand, Chris Strachotta and Jim Jackson (2010), Linking Fundamental Geochemistry And Empirical Observations For Water Quality Predictions Using GoldSim, Brazil, Proceedings of IMWA 2010, "Mine Water and Innovative Thinking", Wolkersdorfer. Ch. and Freund, A., p 313-316, Sydney, Nova Scotia, Canada.
- Ted Eary, Jody Eshleman, Ryan Jakubowski and Andrew Watson (2008), Applying Numerical Hydrochemical Models as Decision Support Tools for Mine Closure Planning, presented at Tailings and Mine Waste ’08, October 19–22, 2008, Vail, Colorado.
- Lisa Wade (2014), A Probabilistic Water Balance, Dissertation for Montana Tech of The University of Montana, Copyright ProQuest, UMI Dissertations Publishing 2014.
- David Ewing Duncan (2003), Do or Die at Yucca Mountain, Wired Magazine, Issue 11.04, April 2003.
- Patrick D. Mattie, Robert G. Knowlton & Bill W. Arnold. (2007). A User’s Guide to the GoldSim/BLT-MS Integrated Software Package: A Low-Level Radioactive Waste Disposal Performance Assessment Model. Sandia Report (SAND2007-1354)
- D. Vopálka, D. Lukin and A. Vokál (2006), Modelling of processes occurring in deep geological repository — development of new modules in the GoldSim environment, Czechoslovak Journal of Physics, Volume 56, Supplement 4 / December, 2006.
- Chris Markley et al. (2011), SOAR: A Model For Scoping Of Options And Analyzing Risk Version 1.0 User Guide, Prepared for U.S. Nuclear Regulatory Commission Contract No. NRC–02–07–006, August 2011.
- Jose Luis Cormenzana (2013), Probabilistic Sensitivity Analysis for the “Initial Defect in the Canister” Reference Model, Workreport 2013-25, Posiva Oy, Eurajoki, Finland.
- Philip H. Stauffer, Hari S. Viswanathan, Rajesh J. Pawar and George D. Guthrie (2009), A System Model for Geologic Sequestration of Carbon Dioxide, Environ. Sci. Technol., 2009, 43 (3), pp 565–570.
- Donovan L. Mathias, Susie Go, Ken Gee, and Scott Lawrence (2008), Simulation Assisted Risk Assessment Applied to Launch Vehicle Conceptual Design, NASA Center for AeroSpace Information (CASI).
- Steven P. Miller, Jennifer E. Granata and Joshua S. Stein (2012), The Comparison of Three Photovoltaic System Designs Using the Photovoltaic Reliability and Performance Model (PV-RPM), Sandia Report SAND2012-10342, Sandia National Laboratories, Albuquerque, New Mexico.
- Golder Associates Launches Independent Software Company Based on GoldSim Software (2004), Water & Wastes DIGEST
- Probabilistic Simulation. GoldSim website.