The purpose of this Software Platform Evaluation (SPE) is to document the top-level evaluation of potential software platforms on which to construct a simulation model that satisfies the requirements for a Verifiable Fuel Cycle Simulation Model (VISION) of the Advanced Fuel Cycle (AFC). See the Software Requirements Specification for Verifiable Fuel Cycle Simulation (VISION) Model (INEEL/EXT-05-02643, Rev. 0) for a discussion of the objective and scope of the VISION model. VISION is intended to serve as a broad systems analysis and study tool applicable to work conducted as part of the AFCI (including costs estimates) and Generation IV reactor development studies. This document will serve as a guide for selecting the most appropriate software platform for VISION. This is a "living document" that will be modified over the course of the execution of this work.

The purpose of this document is to provide a guide for using the current version of the Verifiable Fuel Cycle Simulation (VISION) model. This is a complex model with many parameters; the user is strongly encouraged to read this user guide before attempting to run the model. This model is an R & D work in progress and may contain errors and omissions. It is based upon numerous assumptions. This model is intended to assist in evaluating "what if" scenarios and in comparing fuel, reactor, and fuel processing alternatives at a systems level for U.S. nuclear power. The model is not intended as a tool for process flow and design modeling of specific facilities nor for tracking individual units of fuel or other material through the system. The model is intended to examine the interactions among the components of a fuel system as a function of time varying system parameters; this model represents a dynamic rather than steady-state approximation of the nuclear fuel system. VISION models the nuclear cycle at the system level, not individual facilities, e.g., "reactor types" not individual reactors and "separation types" not individual separation plants. Natural uranium can be enriched, which produces enriched uranium, which goes into fuel fabrication, and depleted uranium (DU), which goes into storage. Fuel is transformed (transmuted) in reactors and then goes into a storage buffer. Used fuel can be pulled from storage into either separation of disposal. If sent to separations, fuel is transformed (partitioned) into fuel products, recovered uranium, and various categories of waste. Recycled material is stored until used by its assigned reactor type. Note that recovered uranium is itself often partitioned: some RU flows with recycled transuranic elements, some flows with wastes, and the rest is designated RU. RU comes out of storage if needed to correct the U/TRU ratio in new recycled fuel. Neither RU nor DU are designated as wastes. VISION is comprised of several Microsoft Excel input files, a Powersim Studio core, and several Microsoft Excel output files. All must be co-located in the same folder on a PC to function. We use Microsoft Excel 2003 and have not tested VISION with Microsoft Excel 2007. The VISION team uses both Powersim Studio 2005 and 2009 and it should work with either.

The purpose of this document is to provide a guide for using the current version of the Verifiable Fuel Cycle Simulation (VISION) model. This is a complex model with many parameters and options; the user is strongly encouraged to read this user guide before attempting to run the model. This model is an R & D work in progress and may contain errors and omissions. It is based upon numerous assumptions. This model is intended to assist in evaluating 'what if' scenarios and in comparing fuel, reactor, and fuel processing alternatives at a systems level. The model is not intended as a tool for process flow and design modeling of specific facilities nor for tracking individual units of fuel or other material through the system. The model is intended to examine the interactions among the components of a fuel system as a function of time varying system parameters; this model represents a dynamic rather than steady-state approximation of the nuclear fuel system. VISION models the nuclear cycle at the system level, not individual facilities, e.g., 'reactor types' not individual reactors and 'separation types' not individual separation plants. Natural uranium can be enriched, which produces enriched uranium, which goes into fuel fabrication, and depleted uranium (DU), which goes into storage. Fuel is transformed (transmuted) in reactors and then goes into a storage buffer. Used fuel can be pulled from storage into either separation or disposal. If sent to separations, fuel is transformed (partitioned) into fuel products, recovered uranium, and various categories of waste. Recycled material is stored until used by its assigned reactor type. VISION is comprised of several Microsoft Excel input files, a Powersim Studio core, and several Microsoft Excel output files. All must be co-located in the same folder on a PC to function. You must use Powersim Studio 8 or better. We have tested VISION with the Studio 8 Expert, Executive, and Education versions. The Expert and Education versions work with the number of reactor types of 3 or less. For more reactor types, the Executive version is currently required. The input files are Excel2003 format (xls). The output files are macro-enabled Excel2007 format (xlsm). VISION 3.4 was designed with more flexibility than previous versions, which were structured for only three reactor types - LWRs that can use only uranium oxide (UOX) fuel, LWRs that can use multiple fuel types (LWR MF), and fast reactors. One could not have, for example, two types of fast reactors concurrently. The new version allows 10 reactor types and any user-defined uranium-plutonium fuel is allowed. (Thorium-based fuels can be input but several features of the model would not work.) The user identifies (by year) the primary fuel to be used for each reactor type. The user can identify for each primary fuel a contingent fuel to use if the primary fuel is not available, e.g., a reactor designated as using mixed oxide fuel (MOX) would have UOX as the contingent fuel. Another example is that a fast reactor using recycled transuranic (TRU) material can be designated as either having or not having appropriately enriched uranium oxide as a contingent fuel. Because of the need to study evolution in recycling and separation strategies, the user can now select the recycling strategy and separation technology, by year.

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures contains the plenary lectures and papers presented at the 11th International Conference on STRUCTURAL SAFETY AND RELIABILITY (ICOSSAR2013, New York, NY, USA, 16-20 June 2013), and covers major aspects of safety, reliability, risk and life-cycle performance of str

As the global demand for energy grows, many nations are considering developing or increasing nuclear capacity as a viable, long-term power source. To assess the possible expansion of nuclear power and the intricate relationships0́4which cover the range of economics, security, and material supply and demand0́4between established and aspirant nuclear generating entities requires models and system analysis tools that integrate all aspects of the nuclear enterprise. Computational tools and methods now exist across diverse research areas, such as operations research and nuclear engineering, to develop such a tool. This dissertation aims to develop methodologies and employ and expand on existing sources to develop a multipurpose tool to analyze international nuclear fuel supply options. The dissertation is comprised of two distinct components: the development of the Material, Economics, and Proliferation Assessment Tool (MEPAT), and analysis of fuel cycle scenarios using the tool. Development of MEPAT is aimed for unrestricted distribution and therefore uses publicly available and open-source codes in its development when possible. MEPAT is built using the Powersim Studio platform that is widely used in systems analysis. MEPAT development is divided into three modules focusing on: material movement; nonproliferation; and economics. The material movement module tracks material quantity in each process of the fuel cycle and in each nuclear program with respect to ownership, location and composition. The material movement module builds on techniques employed by fuel cycle models such as the Verifiable Fuel Cycle Simulation (VISION) code developed at the Idaho National Laboratory under the Advanced Fuel Cycle Initiative (AFCI) for the analysis of domestic fuel cycle. Material movement parameters such as lending and reactor preference, as well as fuel cycle parameters such as process times and material factors are user-specified through a Microsoft Excel © data spreadsheet. The material movement module is the largest of the three, and the two other modules that assess nonproliferation and economics of the options are dependent on its output. Proliferation resistance measures from literature are modified and incorporated in MEPAT. The module to assess the nonproliferation of the supply options allows the user to specify defining attributes for the fuel cycle processes, and determines significant quantities of materials as well as measures of proliferation resistance. The measure is dependent on user-input and material information. The economics module allows the user to specify costs associated with different processes and other aspects of the fuel cycle. The simulation tool then calculates economic measures that relate the cost of the fuel cycle to electricity production. The second part of this dissertation consists of an examination of four scenarios of fuel supply option using MEPAT. The first is a simple scenario illustrating the modules and basic functions of MEPAT. The second scenario recreates a fuel supply study reported earlier in literature, and compares MEPAT results with those reported earlier for validation. The third, and a rather realistic, scenario includes four nuclear programs with one program entering the nuclear energy market. The fourth scenario assesses the reactor options available to the Hashemite Kingdom of Jordan, which is currently assessing available options to introduce nuclear power in the country. The methodology developed and implemented in MEPAT to analyze the material, proliferation and economics of nuclear fuel supply options is expected to help simplify and assess different reactor and fuel options available to utilities, government agencies and international organizations.

The Nuclear Fuel Cycle Simulation System (VISTA) is a simulation system which estimates long term nuclear fuel cycle material and service requirements as well as the material arising from the operation of nuclear fuel cycle facilities and nuclear power reactors. It is a scenario based simulation tool which can model several nuclear fuel cycle options including existing nuclear power reactor types and future possible reactor types. The past operations of the power reactors and fuel cycle facilities can be modelled in the system, in order to estimate the current amount of spent fuel stored or total Pu in stored spent fuel. It can also accept future projections for nuclear power and other scenario parameters in order to predict future fuel cycle material requirements.The model has been designed to be an optimum mixture of simplicity, speed and accuracy. It does not require too many input parameters if the purpose is just to compare the requirements for selected scenarios. Furthermore, the accuracy of the system can be improved by introducing more detailed and correct sets of input parameters.

This easy to read text provides a broad introduction to the fundamental concepts of modeling and simulation (M&S) and systems engineering, highlighting how M&S is used across the entire systems engineering lifecycle. Features: reviews the full breadth of technologies, methodologies and uses of M&S, rather than just focusing on a specific aspect of the field; presents contributions from specialists in each topic covered; introduces the foundational elements and processes that serve as the groundwork for understanding M&S; explores common methods and methodologies used in M&S; discusses how best to design and execute experiments, covering the use of Monte Carlo techniques, surrogate modeling and distributed simulation; explores the use of M&S throughout the systems development lifecycle, describing a number of methods, techniques, and tools available to support systems engineering processes; provides a selection of case studies illustrating the use of M&S in systems engineering across a variety of domains.