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A digital, wide-range, neutron detection (WRND) system in a compact, VME form factor monitors neutron activity within the core of a nuclear reactor across the reactor's entire operating range.
The International Space Station Water Processor Assembly provides contaminant control and deionization to the Water Recovery System. The Water Processor Assembly presently utilizes sorbent-based Multifiltration Beds and a downstream Catalytic Reactor for these operations. Upgrades and process improvements are desired to improve performance, increase reliability, and decrease consumable resupply. To this end, reverse osmosis membrane separation technologies were evaluated to reduce influent contaminant loads, candidate additives to inhibit wastewater biofilm formation were studied, and life stability testing was completed for a recently developed high-activity catalyst. Evaluation of an adsorption media integration concept was also completed. The performance and applicability of these new technologies within the Water Processor Assembly, as well as their suitability for exploration missions, are discussed herein.
Heat pipe reactors have been considered by the Space Nuclear Propulsion program for Nuclear Electric Propulsion (NEP) power conversion systems and will require the use of heat exchangers to transfer heat via heat pipes to the Brayton working fluid from the reactor. Sodium (Na) and lithium (Li) were considered as viable working fluids inside the heat pipes which were assumed to have the same geometry based on studies and information from the Los Alamos National Laboratory. The heat exchanger was assumed to be a rectangular duct with heat pipes serving as tubes from previous NEP work and recommendations. Based on this geometry, Zukauskas correlations were used to model the convective heat transfer and pressure losses. Parametric sizing of the reactor component involved operational limits-based heat pipe thermal hydraulic modeling in cohesion with required user input geometry for the in-core lattice and various subcomponents. This work considered various power conversion inlet temperatures (PCIT) of 1100 K, 1150 K, and 1200 K for Na heat pipes and 1100 K, 1150 K, 1200 K, and 1400 K for Li heat pipes based on recommendations from prior work. Using these different PCITs, the subsystem masses and pressure losses were determined and analyzed. Na showed a lower overall operating temperature and about a fifth of the maximum heat throughput capability than that of Li for the same geometry. Due to this, the entire Na-based subsystem ended up being three times more massive than the Li-based subsystem given five times the required number of heat pipes. At the low PCIT of 1100 K, the Na-based subsystem exhibited the lowest pressure losses given the large overall cross sectional flow area and relatively low frictional pressure losses. However, as the PCIT increased, the frictional pressure losses increased resulting in higher pressure losses at the 1200 K PCIT than Li-based subsystem. However, the Li-based subsystem exhibited the largest pressure losses of all analyzed cases at the 1400 K PCIT due to the low density of the Brayton working fluid at this temperature.
Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this article, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation • Virtual sensing • Sensor response-time assessment These algorithms incorporate, at their base, a Gaussian Process-based uncertainty quantification (UQ) method. Various plant models (using kernel regression, GP, or hierarchical models) may be used to predict sensor responses under various plant conditions. These predicted responses can then be applied in fault detection (sensor output and response time) and in computing the correct value (virtual sensing) of a failing physical sensor. The methods being evaluated in this work can compute confidence levels along with the predicted sensor responses, and as a result, may have the potential for compensating for sensor drift in real-time (online recalibration). Evaluation was conducted using data from multiple sources (laboratory flow loops and plant data). Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.
Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. Specifically, the next generation of OLM technology is expected to include newly developed advanced algorithms that improve monitoring of sensor/system performance and enable the use of plant data to derive information that currently cannot be measured. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this paper, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation – fault detection and selection of acceptance criteria • Virtual sensing – signal value prediction and acceptance criteria • Response-time assessment – fault detection and acceptance criteria selection A GP-based uncertainty quantification (UQ) method previously developed for UQ in OLM, was adapted for use in sensor-fault detection and virtual sensing. For signal validation, the various components to the OLM residual (which is computed using an AAKR model) were explicitly defined and modeled using a GP. Evaluation was conducted using flow loop data from multiple sources. Results using experimental data from laboratory-scale flow loops indicate that the approach, while capable of detecting sensor drift, may be incapable of discriminating between sensor drift and model inadequacy. This may be due to a simplification applied in the initial modeling, where the sensor degradation is assumed to be stationary. In the case of virtual sensors, the GP model was used in a predictive mode to estimate the correct sensor reading for sensors that may have failed. Results have indicated the viability of using this approach for virtual sensing. However, the GP model has proven to be computationally expensive, and so alternative algorithms for virtual sensing are being evaluated. Finally, automated approaches to performing noise analysis for extracting sensor response time were developed. Evaluation of this technique using laboratory-scale data indicates that it compares well with manual techniques previously used for noise analysis. Moreover, the automated and manual approaches for noise analysis also compare well with the current “gold standard”, hydraulic ramp testing, for response time monitoring. Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.
Best-estimate fuel performance codes such as BISON currently under development at the Idaho National Laboratory, utilize empirical and mechanistic lower-length-scale informed correlations to predict fuel behavior under normal operating and accident reactor conditions. Traditionally, best-estimate results are presented using the correlations with no quantification of the uncertainty in the output metrics of interest. However, there are associated uncertainties in the input parameters and correlations used to determine the behavior of the fuel and cladding under irradiation. Therefore, it is important to perform uncertainty quantification and include confidence bounds on the output metrics that take into account the uncertainties in the inputs. In addition, sensitivity analyses can be performed to determine which input parameters have the greatest influence on the outputs. In this paper we couple the BISON fuel performance code to the DAKOTA uncertainty analysis software to analyze a representative fuel performance problem. The case studied in this paper is based upon rod 1 from the IFA-432 integral experiment performed at the Halden Reactor in Norway. The rodlet is representative of a BWR fuel rod. The input parameters uncertainties are broken into three separate categories including boundary condition uncertainties (e.g., power, coolant flow rate), manufacturing uncertainties (e.g., pellet diameter, cladding thickness), and model uncertainties (e.g., fuel thermal conductivity, fuel swelling). Utilizing DAKOTA, a variety of statistical analysis techniques are applied to quantify the uncertainty and sensitivity of the output metrics of interest. Specifically, we demonstrate the use of sampling methods, polynomial chaos expansions, surrogate models, and variance-based decomposition. The output metrics investigated in this study are the fuel centerline temperature, cladding surface temperature, fission gas released, and fuel rod diameter. The results highlight the importance of quantifying the uncertainty and sensitivity in fuel performance modeling predictions and the need for additional research into improving the material models that are currently available.
The Coal-Direct Chemical Looping (CDCL) process is emerging as a promising technology capable of high efficiency for producing electricity with low cost for carbon capture. In the CDCL process, coal is fully combusted to form carbon dioxide (CO2) and water via reaction with Fe2O3-based oxygen-carrier particles in a moving-bed reducer reactor. During reaction with coal, the Fe2O3 is reduced to form a mixture of Fe and FeO. The reduced particles are then transported to the combustor reactor where they are re-oxidized with air. The oxidation of the Fe and FeO mixture generates heat that can be used to produce steam for power generation. Once the particles are fully regenerated, they are pneumatically recycled to the reducer reactor where another reduction-oxidation (Redox) cycle begins. The Babcock & Wilcox Company (B&W) and The Ohio State University (OSU) have been developing the CDCL technology based on iron-based metal oxide particles circulating in a moving-bed reducer coupled with an entrained-flow combustor. As part of a Phase I effort of a DOE-sponsored project, B&W identified scale-up technology gaps for the coal-direct chemical looping reactor process, most of which fall under the moving-bed reactor design and operation. However, after the Phase I research, B&W concluded that the CDCL technology has the potential of generating electricity while meeting the DOE goals of greater than 90% CO2 capture and less than 35% increase in levelized cost of electricity (LCOE). B&W then moved to a Phase II project to design, build and demonstrate the CDCL technology at a 250 kWth capacity. B&W finished the construction and commissioning of the pilot test facility in 2017. The approach used to design a 250 kWth pilot-scale CDCL facility to investigate the technology gaps is presented. Important lessons from the commissioning activities that would transfer to commercial plant start-up are also shared. The findings from the test program are being incorporated into a Pre-FEED study for a 10 MWe large pilot demonstration. Under a separately-funded heat integration project, B&W and OSU are developing a multiphase computational fluid dynamic model (MFIX) of the combustor to quantify heat transfer to embedded steam generation surface, utilizing pinch analysis (ASPEN Energy Analyzer) to identify potential improvements of the heat integration scheme, and developing an integrated dynamic model (ProTRAX) of the steam turbine coupled with the chemical looping process to identify potential start-up and transient operation issues. Under an advanced control system for optimizing operation, B&W is supporting OSU to install a rules-based optimizer (B&W’s FocalPoint) on their 25 kWt bench-scale facility on the OSU West Campus. Finally, B&W and OSU were recently selected to perform a Phase I Feasibility study to evaluate installation of the 10 MWe pilot at the Dover Power & Light Municipal Power Plant in Dover, Ohio. The feasibility study includes compiling an Environmental Information Volume and generating a cost estimate for the design, construction and commissioning of the plant as well as lining up commitments from potential project participants and funding contributors. Updates on each project will be presented. The status of the technology development will be discussed. We focus on the progress made to date towards closing the technology gaps under each project.
The Coal-Direct Chemical Looping (CDCL) process is emerging as a promising technology capable of high efficiency for producing electricity with low cost for carbon capture. In the CDCL process, coal is fully combusted to form carbon dioxide (CO2) and water via reaction with Fe2O3-based oxygen-carrier particles in a moving-bed reducer reactor. During reaction with coal, the Fe2O3 is reduced to form a mixture of Fe and FeO. The reduced particles are then transported to the combustor reactor where they are re-oxidized with air. The oxidation of the Fe and FeO mixture generates heat that can be used to produce steam for power generation. Once the particles are fully regenerated, they are pneumatically recycled to the reducer reactor where another reduction-oxidation (Redox) cycle begins. The Babcock & Wilcox Company (B&W) and The Ohio State University (OSU) have been developing the CDCL technology based on iron-based metal oxide particles circulating in a moving-bed reducer coupled with an entrained-flow combustor. As part of a Phase I effort of a DOE-sponsored project, B&W identified scale-up technology gaps for the coal-direct chemical looping reactor process, most of which fall under the moving-bed reactor design and operation. However, after the Phase I research, B&W concluded that the CDCL technology has the potential of generating electricity while meeting the DOE goals of greater than 90% CO2 capture and less than 35% increase in levelized cost of electricity (LCOE). B&W then moved to a Phase II project to design, build and demonstrate the CDCL technology at a 250 kWth capacity. B&W finished the construction and commissioning of the pilot test facility in 2017. The approach used to design a 250 kWth pilot-scale CDCL facility to investigate the technology gaps is presented. Important lessons from the commissioning activities that would transfer to commercial plant start-up are also shared. The findings from the test program are being incorporated into a Pre-FEED study for a 10 MWe large pilot demonstration. Under a separately-funded heat integration project, B&W and OSU are developing a multiphase computational fluid dynamic model (MFIX) of the combustor to quantify heat transfer to embedded steam generation surface, utilizing pinch analysis (ASPEN Energy Analyzer) to identify potential improvements of the heat integration scheme, and developing an integrated dynamic model (ProTRAX) of the steam turbine coupled with the chemical looping process to identify potential start-up and transient operation issues. Under an advanced control system for optimizing operation, B&W is supporting OSU to install a rules-based optimizer (B&W’s FocalPoint) on their 25 kWt bench-scale facility on the OSU West Campus. Finally, B&W and OSU were recently selected to perform a Phase I Feasibility study to evaluate installation of the 10 MWe pilot at the Dover Power & Light Municipal Power Plant in Dover, Ohio. The feasibility study includes compiling an Environmental Information Volume and generating a cost estimate for the design, construction and commissioning of the plant as well as lining up commitments from potential project participants and funding contributors. Updates on each project will be presented. The status of the technology development will be discussed. We focus on the progress made to date towards closing the technology gaps under each project.
A method is provided for coating the substrate of a component, such as a zirconium alloy cladding tube, for use in a water cooled nuclear reactor under normal operating conditions and under high temperature oxidation conditions. The method includes heating a pressurized carrier gas to a temperature between 200° C. and 1200° C., adding chromium or chromium-based alloy particles having an average diameter of 20 microns or less to the heated carrier gas, and spraying the carrier gas and particles onto the substrate at a velocity, preferably from 800 to 4000 ft./sec. (about 243.84 to 1219.20 meters/sec.), to form a chromium and/or chromium-based alloy coating on the substrate to a desired thickness.
The safe and efficient operation of nuclear reactors require accurate knowledge of peak temperatures in the fuel assemblies. The temperature profiles are governed by the thermal transport properties of the fuel, namely the thermal conductivities (k) and thermal diffusivities (D). These values can be very difficult to measure as they are known to vary considerably from the measured bulk values of the fresh fuel, and quickly degrade with increasing burnup [1 -3] . Laser-based techniques have been effectively used for non-destructive and non-contact thermal transport measurements of a wide variety of materials, including nuclear materials [4-6] that would otherwise prove too hazardous or difficult to measure otherwise. In this study, a new thermoreflectance technique known as square-pulse transient thermoreflectance (SPTR) is described and used to determine the mesoscale thermal diffusivity of both uranium sesquisilicide (U3Si2) and uranium nitride (UN) phases in a composite fuel with micron level spatial resolution [7]. This technique employs a rapid train of square-wave pulses from an excitation laser to create a periodic heat flux on a gold coated sample surface. Surface adsorption results in transient film temperatures and hence rapid fluctuations in thermoreflectance that can be measured via a detection laser coupled with a digital oscilloscope. The lasers are coaxially focused on the sample surface, allowing for a sample measurement area of a single convolved laser spot size (~2 µm). A sensitivity analysis was conducted to identify key measurement parameters of this technique using reference materials with a range of thermal conductivities comparable to those of both ceramic, composite, and metal nuclear fuel types (1.4 – 27.2 W/m-K). The reference materials were measured using the new technique as well as a spatial-domain thermoreflectance technique (SDTR) previously reported for a comparison [8]. Additionally, measurements of several U3Si2 and UN phase regions of a polished UN/U3Si2 (70/30 vol.%) sample were taken, and the resulting calculated D values are reported, with both techniques showing excellent agreement between samples. This technique was used to scan a multiphase region at 5 micron increments to generate a local diffusivity map, demonstrating the utility of the techniques for measuring thermal transport properties in specimens with precipitates and secondary phases. Furthermore, the technique is currently being applied to measure thermal properties of Fast Flux Test Reactor (FFTR) irradiated metal fuel specimens whose porous microstructure make it very difficult to measure using other techniques. Comparison of the local scale measurements are compared with the pre-irradiated fuel samples to show the degradation of thermal transport in fuels due to pores from fission gas bubbles and other irradiation induced defects.
A zirconium alloy cladding tube for use in a water cooled nuclear reactor under normal operating conditions and under high temperature oxidation conditions is described. The cladding tube has a coating uniformly deposited thereon. The coating, which may be up to 300 microns thick, is selected from the group consisting of chromium, a chromium-based alloy, and combinations thereof.
Best-estimate fuel performance codes such as BISON currently under development at the Idaho National Laboratory, utilize empirical and mechanistic lower-length-scale informed correlations to predict fuel behavior under normal operating and accident reactor conditions. Traditionally, best-estimate results are presented using the correlations with no quantification of the uncertainty in the output metrics of interest. However, there are associated uncertainties in the input parameters and correlations used to determine the behavior of the fuel and cladding under irradiation. Therefore, it is important to perform uncertainty quantification and include confidence bounds on the output metrics that take into account the uncertainties in the inputs. In addition, sensitivity analyses can be performed to determine which input parameters have the greatest influence on the outputs. In this paper we couple the BISON fuel performance code to the DAKOTA uncertainty analysis software to analyze a representative fuel performance problem. The case studied in this paper is based upon rod 1 from the IFA-432 integral experiment performed at the Halden Reactor in Norway. The rodlet is representative of a BWR fuel rod. The input parameters uncertainties are broken into three separate categories including boundary condition uncertainties (e.g., power, coolant flow rate), manufacturing uncertainties (e.g., pellet diameter, cladding thickness), and model uncertainties (e.g., fuel thermal conductivity, fuel swelling). Utilizing DAKOTA, a variety of statistical analysis techniques are applied to quantify the uncertainty and sensitivity of the output metrics of interest. Specifically, we demonstrate the use of sampling methods, polynomial chaos expansions, surrogate models, and variance-based decomposition. The output metrics investigated in this study are the fuel centerline temperature, cladding surface temperature, fission gas released, and fuel rod diameter. The results highlighted the importance of the thermal conductivity of the fuel, the thermal conductivity of the cladding, the gap thickness, the fuel thermal expansion, and the system power factor as influential parameters.
Westinghouse has accumulated significant know-how in the reactor dismantling activities. The approach to internals segmentation and packaging has been continuously enhanced by incorporating lessons learned and best practices into each successive project. Building on the most recent successful experience at Jose Cabrera in Spain, Westinghouse has won several major awards with 13 reactors currently under contract at Chooz A (France), Barsebaeck 1-2 (Sweden), Bohunice V1 (two units in Slovakia), Neckarwestheim I, Philippsburg 1, and more recently at Unterweser, Grafenrheinfeld, Grohnde, Isar 1, Isar 2, Brokdorf (Germany). The primary challenges of a segmentation and packaging project are to separate the highly activated materials from the less-activated materials and package them into appropriate containers for disposal. The choice of the optimum cutting technology is important for a successful project implementation and depends on some specific constraints like disposal costs, project schedule, available work areas or safety. The selected technology for all recent Westinghouse projects is mechanical cutting with different types of designed cutting equipment (hydraulic shears, band saw, disc saw). All segmentation tools are remotely controlled due to the high radiation levels and ALARA and personal safety is the number one priority during the challenging site work. Training on mock-ups in the Westinghouse facility before going to the site is a very important step in order to optimize all procedures and thereby maximize the efficiency on site in order to finalize the project on time or before. The work covers the segmentation of reactor internals but also, in some cases, some replaced components during plant operation that were stored in the spent fuel pool. The cutting equipment proved to be reliable and flexible which is very valuable for the execution of such a challenging project. The Barsebaeck Unit 2 site work has been finished end of October 2017 and its lessons learned are taken into consideration for future activities. Site work is currently on-going simultaneously at Chooz A, Barsebaeck 1, Neckarwestheim I and Philippsburg 1. This paper will provide an overview of all these on-going segmentation projects and will focus on feedback from the first projects executed on site. (authors)
Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. Advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this paper, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. A Gaussian Process (GP)-based uncertainty quantification (UQ) method previously developed for UQ in OLM, was adapted for this purpose. The resulting models are being evaluated for use in high-confidence signal validation for the purpose of detecting and diagnosing sensor faults, and for computing virtual sensor outputs that may be used as a temporary replacement for failing sensors. In addition to assessing sensor drift, approaches for extracting sensor response time in an automated fashion were developed, for monitoring changes in sensor response time in pressure transmitters. Such changes are also indicative of various fault modes. These algorithms were evaluated with existing measurement data from several laboratory-scale flow loops. Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants. (authors)
The Transient Reactor Test Facility (TREAT) has historically provided the capability for transient fuel performance and safety testing in the United States. The operation of this facility was suspended in 1994. However, in recent years preparations for the restart of TREAT were initiated. TREAT is a graphite moderated and air cooled reactor. Its core is composed of a 19 x 19 array of fuel assemblies that contain UO{sub 2} (HEU) fuel particles dispersed in a graphite matrix, along with reflector assemblies filled with graphite only. Each fuel assembly is 8 feet tall with a fueled region that is 4 feet tall and upper and lower regions of graphite that are 2 feet tall. In addition, a 2 feet thick radial graphite reflector region is present. Finally, a 5 feet thick concrete biological shield surrounds the core. Previous work showed that the graphite thermal scattering cross sections can introduce variations exceeding 2000 pcm in the predicted eigenvalue of the TREAT core. Recognizing that the graphite in the assembly (and thereby the core) is a porous mixture of crystalline and amorphous carbon (with a lower density in comparison to ideal crystalline graphite), it becomes clear that TREAT specific thermal neutron scattering cross section libraries will be needed (at all temperatures of interest) for use within neutronic codes seeking to simulate TREAT. (authors)
In fuel assembly, spacer grids have strong effects on pressure drop and other thermal-hydraulic behaviors. Considering the design and safety operation of a reactor, it is important to predict pressure drop accurately in the fuel assembly. For a mixed core, a reactor with mixed fuel designs in the reactor core, undesired cross flow were often caused by differences in pressure drop among neighboring bundles. The pressure drop is often estimated for designs of new reactor core fuel assemblies. In the past, due to complex flow conditions in fuel assembly, the pressure drop of a fuel assembly relied greatly on costly and time-consuming full length thermal hydraulic tests. Some empirical pressure drop correlations and CFD methodology were applied, however, only limited to a single gird span. Without considering pressure drop in the entire fuel assembly it is quite difficult to optimize the fuel assembly design. Most experimental fuel assemblies consist of mixing vane grids (MVG) and simple support grids (SSG). The MVG's were the main tested spacer grids in the reactor and SSG's were used only in experimental study to support the fuel assembly. In fuel reactor core, the fuel assembly heats the coolant, which could turn the single-phase water system into a two-phase flow system and eventually lead to critical heat flux (CHF). Because of the complexity of phase change, it is very difficult to predict pressure drop in rod bundles with two-phase flow. In this paper, a new method to predict the pressure drop in the fuel assembly is proposed and verified by experimental data.
Provide material solutions to enabling design, construction, licensing and operation of advanced reactors Including Fast Reactors, Gas-cooled Reactors and Molten Salt Reactors (solid or liquid fuel) Could be of modular or micro designs, and from hundreds of MWe to kWe Provide technical bases needed for design and licensing of advanced reactor components Develop & validate improved high temperature design methodology Provide qualification data (to NQA-1 or equivalent) on structural materials Utilize consensus standards when appropriate (e.g., ASME, ASTM, etc.) Target resolution of issues needed for near to mid-term deployment of advanced reactors
As of the end of 2022, it is estimated that over 90,000 metric tons of heavy metal (MTHM) of spent nuclear fuel (SNF) were stored at various commercial nuclear power reactor sites (both operating and shutdown) across the United States [1]. The Office of Storage and Transportation within the U.S. Department of Energy’s Office of Nuclear Energy is planning for the transportation, storage, and eventual disposal of SNF and high-level radioactive waste (HLW). To aid in this effort and inform decision-makers about the backend of the spent fuel cycle, systems analysis tools capable of analyzing the various options with respect to SNF and HLW management are being used as well as continuously improved to meet the evolving needs of the program. System analysts typically use these tools to vary underlying assumptions (shipping rates, available facilities, start dates, interim storage capacity, etc.) and study the associated system implications such as timing for clearing sites of SNF, various cost elements, transportation infrastructure acquisition needs, etc.