GoldSim

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.

GoldSim
Developer(s)GoldSim Technology Group LLC
Stable release
12.1.5 / July 15, 2020 (2020-07-15)
Written inC++
Operating systemWindows
TypeSimulation software
LicenseProprietary
Websitewww.goldsim.com

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 [1] [2] [3] [4] [5] [6], mining [7] [8] [9] [10] [11], radioactive waste management [12] [13] [14] [15] , geological carbon sequestration [16] [17], aerospace mission risk analysis [18] [19] and energy.[20]

History

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.[21]

Notable applications include providing the simulation framework for: 1) the Yucca Mountain Repository Performance Assessment model developed by Sandia National Laboratories;[12] 2) a comprehensive system-level computational model for performance assessment of geological sequestration of CO2 developed by Los Alamos National Laboratory;[16] 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;[4] and 4) models for simulating risks associated with future manned space missions by NASA Ames Research Center.[18][19]

Modeling Environment

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.[22] 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.

gollark: ?!
gollark: PotatOS has a kind of scheduler, a VFS, not Unix philosophy, integrated kernel/OS.
gollark: PotatOS also has öne.
gollark: Anyway. Most OSes aren't. PotatOS and Shutdownos are the only good ones.
gollark: Don't let solar work at Kiwi!

See also

References
  1. Lloyd Townley, Huanhuan Jiang and Jinquan Tang (2019), WRRM1 and WRRM2: Implementations in GoldSim of Unit Process Models and IWA Benchmark Models (BSM1 and BSM2) for Nutrient Removal, Innovation Conference on Sustainable Wastewater Treatment and Resource Recovery, Shanghai, China.
  2. Erfan Goharian and Steven J. Burian (2014), Integrated Urban Water Resources Modeling In A Semi-Arid Mountainous Region Using A Cyberinfrastructure Framework Archived 2014-11-29 at the Wayback Machine, Proceedings of the 11th International Conference on Hydroinformatics, HIC 2014, New York, New York.
  3. James Andrew Griffiths, Fangfang Zhu, Faith Ka Shun Chan and David Laurence Higgitt (2019), Modelling the impact of sea-level rise on urban flood probability in SE China, Geoscience Frontiers, March 2019.
  4. 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.
  5. James C. Schlaman and Danny Johnson (20147, Eliminating the Silo Effect Integrated Water, Wastewater, Watershed Model Helps the Atlanta Region Plan a More Holistic Future, Proceedings of the Water Environment Federation, January 2017.
  6. Erfan Goharian, Steven J. Burian, Jason Lillywhite, and Ryan Hile (2016), Vulnerability Assessment to Support Integrated Water Resources Management of Metropolitan Water Supply Systems, Journal of Water Resources Planning and Management, November 2016.
  7. Brent C. Johnson, Pamela Rohal, and Ted Eary (2018), Coupling PHREEQC with GoldSim for a More Dynamic Water Modeling Experience, 11th ICARD | IMWA | WISA MWD 2018 Conference – Risk to Opportunity, January 2018 Pretoria, South Africa.
  8. Nick Martin and Michael Gabora (2018), Modelling Complex Mine Water Closure Challenges using a Coupled FEFLOW-GoldSim Model, 11th ICARD | IMWA | WISA MWD 2018 Conference – Risk to Opportunity, January 2018 Pretoria, South Africa.
  9. Lisa Wade (2014), A Probabilistic Water Balance, Dissertation for Montana Tech of The University of Montana, Copyright ProQuest, UMI Dissertations Publishing 2014.
  10. Valérie Plagnes, Brad Schmid, Brett Mitchell and Ian Judd-Henrey (2017), Water Balance Modelling of a Uranium Mill Effluent Management System, Journal of Hydrology, June 2017.
  11. William Schafer, John Barber, Manuel Contreras and Jesus Tellez (2016), Integrating Surface Water Load Modelling into Mine Closure Performance Evaluation, International Mine Water Association Conference Proceedings, July 2016.
  12. David Ewing Duncan (2003), Do or Die at Yucca Mountain, Wired Magazine, Issue 11.04, April 2003.
  13. K.P. Lee, R. Andrews, N. Hasan, R. Senger, M. Kozak, A. K. Wahi, and W. Zhou (2018),Integration of Models for the Hanford Integrated Disposal Facility Performance Assessment, Proceedings of the 2018 Waste Management Symposium, March 2018.
  14. Jongtae Jeong, Youn-Myoung Lee, Jung-Woo Kim, Dong-Keun Cho, Nak Yul Ko, and Min Hoon Baik (2016), Progress of the Long-Term Safety Assessment of a Reference Disposal System for High Level Wastes in Korea, Progress in Nuclear Energy, July 2016.
  15. B. Haverkamp, J. Krone, and I. Shybetskyi (2013), Safety Assessment for a Surface Repository in the Chernobyl Exclusion Zone, Proceedings of the 2013 Waste Management Symposium, February 2013.
  16. 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.
  17. Sean Sanguinitoa, Angela L. Goodman, and James I. Sams III (2018), CO2-SCREEN tool: Application to the oriskany sandstone to estimate prospective CO2 storage resource, International Journal of Greenhouse Gas Control, August 2018.
  18. Donovan L. Mathias, Susie Go, and Christopher J. Mattenberger (2014), Engineering Risk Assessment of Space Thruster Challenge Problem, Proceedings, Probabilistic Safety Assessment and Management PSAM 12, Honolulu, HI, June 2014.
  19. Susie Go, Donovan L. Mathias, Scott Lawrence, Ken Gee and Christopher J. Mattenberger (2014), An Integrated Reliability and Physics-based Risk Modeling Approach for Assessing Human Spaceflight Systems, Proceedings, Probabilistic Safety Assessment and Management PSAM 12, Honolulu, HI, June 2014.
  20. 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.
  21. Golder Associates Launches Independent Software Company Based on GoldSim Software (2004), Water & Wastes DIGEST
  22. Probabilistic Simulation. GoldSim website.
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