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At least 1,423 records · Page 79

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NASA NTRSDate not supplied

Reconstruction of Thermal Protection System Aeroheating using a Green’s Function Approach

Inverse heat transfer (IHT) techniques are often used to reconstruct the surface heating conditions on spacecraft thermal protection systems (TPS) during atmospheric entry. Current IHT techniques for entry spacecraft applications, however, demand substantial computational resources, and are impractical for analyses such as uncertainty quantification and real-time health monitoring. In this paper, a Green’s function sensor fusion approach is used to reconstruct the TPS surface aeroheating conditions on experimental spaceflight and ground test systems from collocated temperature and heat flux sensors embedded in the TPS. The algorithm leverages Green’s functions to model the heat conduction within the spacecraft TPS and stabilizes the recovery of the surface heating condition using the direct heat flux sensor measurement. The algorithm is validated using arc-jet ground test data and applied to the reconstruction of the Mars 2020 backshell heating during Martian atmospheric entry. The performance of the algorithm is benchmarked against a current state-of-the-art IHT framework, FIAT_Opt. The Green’s function-based reconstruction algorithm recovers the net hot-wall heat flux absorbed by the TPS and the incident heat flux from the atmospheric entry environment in close agreement with FIAT_Opt. Notably, computation of the surface heating condition is completed in three orders of magnitude less time with the Green’s function sensor fusion approach using a consumer-grade PC, versus with FIAT_Opt running on a high performance computer cluster. The efficiency of the algorithm is leveraged to compute the uncertainty contributions of input parameters to the total uncertainty in reconstructed Mars 2020 backshell heating for the full atmospheric entry heat pulse. The sensitivity analysis uncovers that, at different times throughout the entry heat pulse, uncertainties in the TPS specific heat, thermal conductivity, and emissivity are all dominant drivers of the reconstruction uncertainty. These results demonstrate Green’s functions and sensor-fusion techniques as promising IHT approaches to reconstruct atmospheric entry environments from TPS-embedded measurements, and highlight how these techniques may give access to post-flight analyses previously hindered by the prohibitive cost of current methods.

Kenneth McAfee↗

NASA NTRS2025

The Global Seasonal Relationship Between Satellite-Observed Cold Pools and Rainfall

Convective cold pools are important modulators of the onset and evolution of deep convection in the tropics. This work leverages a dataset derived from the Advanced Scatterometer (ASCAT) satellite instrument by Garg et al. (2020) to quantify seasonal variations in cold pool activity and their relationship to deep convection across tropical ocean basins. The dataset identifies gradient features (GFs) in the surface wind field, which have been shown to serve as reliable proxies for the boundaries of atmospheric cold pools. We examine the relationship between GFs and climatologies of precipitation, column relative humidity (CRH), and bulk vertical wind shear. We also collocate GFs with precipitation and CRH. High GF frequency, precipitation, and CRH coincide in many regions of the tropics, consistent with our understanding of the physical connections between precipitation and cold pool generation. On the other hand, climatological bulk wind shear is often low in convective regions, and there is a weak inverse correlation between GF frequency and bulk wind shear, while our prior expectation might have been that shear promotes cold pool formation. Compared to GF frequency, GF size shows a weaker relationship with the convective environment, with some of the largest GF sizes occurring at lower CRH values for a given rainfall rate. In a few exceptional regions and seasons, such as the Indian Ocean in northern hemisphere summer, the region of greatest precipitation does not coincide with the region of greatest GF frequency. These cases also have very high seasonal mean CRH, suggesting that in these regions cold pool formation is suppressed by reduced evaporation of precipitation.

Convective cold pools↗

NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗

DOE OSTI2013

Three-Dimensional Geothermal Fairway Mapping: Examples From the Western Great Basin, USA

Elevated permeability along fault systems provides pathways for circulation of geothermal fluids. Accurate location of such fluid flow pathways in the subsurface is crucial to future geothermal development in order to both accurately assess resource potential and mitigate drilling costs by increasing drilling success rates. Employing a variety of surface and subsurface data sets, we present detailed 3D geologic analyses of two Great Basin geothermal systems, the actively producing Brady’s geothermal system and a ‘greenfield’ geothermal prospect at Astor Pass, Nevada. 3D modeling provides the framework for quantitative structural analyses. We combine 3D slip and dilation tendency analysis along fault zones and calculations of fault intersection density in the two geothermal systems with the locations of lithologies capable of supporting dense, interconnected fracture networks. The collocation of these permeability promoting characteristics with elevated heat represent geothermal ‘fairways’, areas with ideal conditions for geothermal fluid flow. Location of geothermal fairways at high resolution in 3D space can help to mitigate the costs of geothermal exploration by providing discrete drilling targets and data-based evaluations of reservoir potential.

15 GEOTHERMAL ENERGY↗

DOE OSTI2016

Improving Lidar Turbulence Estimates for Wind Energy

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. This presentation primarily focuses on the physics-based corrections, which include corrections for instrument noise, volume averaging, and variance contamination. As different factors affect TI under different stability conditions, the combination of physical corrections applied in L-TERRA changes depending on the atmospheric stability during each 10-minute time period. This stability-dependent version of L-TERRA performed well at both sites, reducing TI error and bringing lidar TI estimates closer to estimates from instruments on towers. However, there is still scatter evident in the lidar TI estimates, indicating that there are physics that are not being captured in the current version of L-TERRA. Two options are discussed for modeling the remainder of the TI error physics in L-TERRA: machine learning and lidar simulations. Lidar simulations appear to be a better approach, as they can help improve understanding of atmospheric effects on TI error and do not require a large training data set.

17 WIND ENERGY↗

DOE OSTI2016

ecode - Electron Transport Algorithm Testing v. 1.0

ecode is a Monte Carlo code used for testing algorithms related to electron transport. The code can read basic physics parameters, such as energy-dependent stopping powers and screening parameters. The code permits simple planar geometries of slabs or cubes. Parallelization consists of domain replication, with work distributed at the start of the calculation and statistical results gathered at the end of the calculation. Some basic routines (such as input parsing, random number generation, and statistics processing) are shared with the Integrated Tiger Series codes. A variety of algorithms for uncertainty propagation are incorporated based on the stochastic collocation and stochastic Galerkin methods. These permit uncertainty only in the total and angular scattering cross sections. The code contains algorithms for simulating stochastic mixtures of two materials. The physics is approximate, ranging from mono-energetic and isotropic scattering to screened Rutherford angular scattering and Rutherford energy-loss scattering (simple electron transport models). No production of secondary particles is implemented, and no photon physics is implemented.

Franke, Brian C.↗

DOE OSTI2017

ARM Radiosondes for National Polar-Orbiting Operational Environmental Satellite System Preparatory Project Validation Field Campaign Report

This IOP has been a coordinated effort involving the U.S. Department of Energy (DOE) Atmospheric Radiation (ARM) Climate Research Facility, the University of Wisconsin (UW)-Madison, and the JPSS project to validate SNPP NOAA Unique Combined Atmospheric Processing System (NUCAPS) temperature and moisture sounding products from the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). In this arrangement, funding for radiosondes was provided by the JPSS project to ARM. These radiosondes were launched coincident with the SNPP satellite overpasses (OP) at four of the ARM field sites beginning in July 2012 and running through September 2017. Combined with other ARM data, an assessment of the radiosonde data quality was performed and post-processing corrections applied producing an ARM site Best Estimate (BE) product. The SNPP targeted radiosondes were integrated into the NOAA Products Validation System (NPROVS+) system, which collocated the radiosondes with satellite products (NOAA, National Aeronautics and Space Administration [NASA], European Organisation for the Exploitation of Meteorological Satellites [EUMETSAT], Geostationary Operational Environmental Satellite [GOES], Constellation Observing System for Meteorology, Ionosphere, and Climate [COSMIC]) and Numerical Weather Prediction (NWP forecasts for use in product assessment and algorithm development. This work was a fundamental, integral, and cost-effective part of the SNPP validation effort and provided critical accuracy assessments of the SNPP temperature and water vapor soundings.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Joint UAS-Balloon Activities (JUBA) Field Campaign Report

Using internal investment funds within Sandia National Laboratories’ (SNL) Division 6000, the Joint UAS-Balloon Activity (JUBA) was a collaborative exercise between SNL Organizations 6533 and 6913 (later 8863) to demonstrate simultaneous flights of tethered balloons and unmanned aerial systems (UAS) on the North Slope of Alaska. JUBA UAS and tethered balloon flights were conducted within the Restricted Airspace associated with the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility third ARM Mobile Facility (AMF3) site at Oliktok Point, Alaska. The Restricted Airspace occupies a two-nautical-mile radius around Oliktok Point. JUBA was conducted at the Sandia Arctic Site, which is approximately 2 km east-southeast of the AMF3. JUBA activities occurred from 08/08/17 and 08/10/17. Atmospheric measurements from tethered balloons can occur for a long duration, but offer limited spatial variation. Measurements from UAS could offer increased spatial variability. To understand how balloon and UAS activities could occur together, JUBA activities included the following: Developing aviation safety plans for the simultaneous, collocated operation of tethered balloons and UAS Adapting tethered balloon-based fiber optic distributed temperature sensing (DTS) measurements to a UAS platform Creating solutions to technical challenges such as: Developing a motor system to match the fiber deployment rate with the ascent/descent rate of the UAS Evaluating methods of identifying the surface in the DTS temperature profile Correlating simultaneous balloon and UAS DTS temperature measurements Assessing the effects of a fiber optic rotary joint on the DTS temperature measurements Determining what UAS-based sensors provided the best calibration source for the DTS temperature data.

North Slope of Alaska, arctic processes, unmanned ↗

DOE OSTI2017

Space-time least-squares Petrov-Galerkin projection in nonlinear model reduction.

Our work proposes a space-time least-squares Petrov-Galerkin (ST-LSPG) projection method for model reduction of nonlinear dynamical systems. In contrast to typical nonlinear model-reduction methods that first apply Petrov-Galerkin projection in the spatial dimension and subsequently apply time integration to numerically resolve the resulting low-dimensional dynamical system, the proposed method applies projection in space and time simultaneously. To accomplish this, the method first introduces a low-dimensional space-time trial subspace, which can be obtained by computing tensor decompositions of state-snapshot data. The method then computes discrete-optimal approximations in this space-time trial subspace by minimizing the residual arising after time discretization over all space and time in a weighted ℓ 2 -norm. This norm can be de ned to enable complexity reduction (i.e., hyper-reduction) in time, which leads to space-time collocation and space-time GNAT variants of the ST-LSPG method. Advantages of the approach relative to typical spatial-projection-based nonlinear model reduction methods such as Galerkin projection and least-squares Petrov-Galerkin projection include: (1) a reduction of both the spatial and temporal dimensions of the dynamical system, (2) the removal of spurious temporal modes (e.g., unstable growth) from the state space, and (3) error bounds that exhibit slower growth in time. Numerical examples performed on model problems in fluid dynamics demonstrate the ability of the method to generate orders-of-magnitude computational savings relative to spatial-projection-based reduced-order models without sacrificing accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Air Pollutant Emissions and Regulatory Implications of a Biorefinery Co-Processing Bio-Oil in a Petroleum Refinery: Preprint

Pyrolysis oil, often referred to as bio-oil when derived from biomass through a fast pyrolysis process, has attracted considerable attention because of the high carbonaceous matter and high heating value compared to the original biomass. Pyrolysis oil provides a viable link between the agriculture/forestry and (petro-) chemical industry. Utilizing existing fossil fuel infrastructure by introducing bio-oil in petroleum refineries to produce renewable hydrocarbon fuels (i.e., repurposing existing assets) is appealing due to its relatively low capital requirement. Our analysis examined the air pollutant emissions for a biorefinery utilizing an ex situ catalytic fast pyrolysis process to produce raw bio-oil, with a design capacity of 2,000 dry metric tons of biomass per day. Such a biorefinery is capable of providing sufficient bio-oil to a 4,200 barrels per day capacity of fluidized catalytic cracking unit if the bio-oil is co-processed at 5% (by volume) feed rate. This research is focused on understanding the potential air pollutant regulatory and permitting implications for a refinery co-processing 5% bio-oil with 95% vacuum gas oil in a fluidized catalytic cracking unit for two scenarios: the first case assumes that an ex situ biorefinery is collocated with a petroleum refinery, and the second one assumes that a petroleum refinery will receive bio-oil from a stand-alone biorefinery. We estimated the potential-to-emit values for 4 biorefinery and FCC sizes and assessed technically feasible emission control options that could help to avoid being subject to prevention of significant deterioration requirements due to petroleum refinery modifications to co-process bio-oil. This research fills gaps and overcomes barriers related to air permitting requirements for co-processing bio-oil in existing refineries for various stakeholders. The insights gained from this analysis can facilitate decision making and help expedite permitting processes for petroleum refineries, which seek opportunities to produce lower carbon fuels in their existing infrastructure.

09 BIOMASS FUELS↗

DOE OSTI2019

Air Pollutant Emissions and Regulatory Implications of a Biorefinery Co-Processing Bio-Oil in a Petroleum Refinery

09 BIOMASS FUELS↗

DOE OSTI2020

Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models

We use a conditional Karhunen-Lo` eve (KL) model to quantify and reduce uncertainty in a stochastic partial differential equation (SPDE) problem with partially-known space-dependent coefficient, Y (x). We assume that a small number of Y (x) measurements are available and model Y (x) with a KL expansion. We achieve reduction in uncertainty by conditioning the KL expansion coefficients on measurements. We consider two approaches for conditioning the KL expansion: In Approach 1, we condition the KL model first and then truncate it. In Approach 2, we first truncate the KL expansion and then condition it. We employ the conditional KL expansion together with Monte Carlo and sparse grid collocation methods to compute the moments of the solution of the SPDE problem. Uncertainty of the problem is further reduced by adaptively selecting additional observation locations using two active learning methods. Method 1 minimizes the variance of the PDE coefficient, while Method 2 minimizes the variance of the solution of the PDE. We demonstrate that conditioning leads to dimension reduction of the KL representation of Y (x). For a linear diffusion SPDE with uncertain log-normal coefficient, we show that Approach 1 provides a more accurate approximation of the conditional log-normal coefficient and solution of the SPDE than Approach 2 for the same number of random dimensions in a conditional KL expansion. Furthermore, Approach 2 provides a good estimate for the number of terms of the truncated KL expansion of the conditional field of Approach 1. Finally, we demonstrate that active learning based on Method 2 is more efficient for uncertainty reduction in the SPDE’s states (i.e., it leads to a larger reduction of the variance) than active learning using Method 2.

Conditioned Karhunen-Lo` eve expanion, machine lea↗

DOE OSTI2019

Vectoring into Potential Blind Geothermal Systems in the Granite Springs Valley Area, Western Nevada: Application of the Play Fairway Analysis at Multiple Scales

The Great Basin region of the western USA is capable of generating much greater amounts of geothermal energy than currently produced. Most geothermal resources in this region are blind, and thus the favorable characteristics for geothermal activity must be synthesized and methodologies developed to discover new commercial-grade systems. The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity as a means of identifying promising areas for new development. In the Nevada play fairway project, nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new geothermal potential map of 96,000 km2. Granite Springs Valley in western Nevada is a particularly promising site selected for detailed study. It contains several favorable structural settings, including terminations of major Quaternary normal faults and fault intersections. Geologic, geophysical, and geochemical techniques were employed to define the most likely sites for high permeability and select drilling targets for temperature-gradient holes. Local and intermediate permeability models were revised to reflect results of detailed analyses and generate new detailed play fairway maps of the area. The most promising site lies in the northeastern part of the basin directly east of Adobe Flat, where the horse-tailing termination of a major normal fault (as revealed by new gravity data), opaline sinter deposits, warm shallow wells, and a low-resistivity anomaly are collocated. Geothermometry suggests a blind system with temperatures as high as ~130°C in this area. Four new temperature-gradient holes document temperatures of ~80°C at ~150 m depth. Lessons learned in the detailed studies of this project include: 1) initially identified sites commonly include multiple favorable settings at a finer scale; 2) promising sites in Cenozoic basins cannot be well defined without detailed geophysical surveys; and 3) play fairway analysis is critical at multiple scales, providing a means to select regional prospects as well as vectoring into drilling targets at individual sites.

15 GEOTHERMAL ENERGY↗

DOE OSTI2018

Discovery and analysis of a blind geothermal system in southeastern Gabbs Valley, Western Nevada

This study assessed the potential for high-temperature (≥130°C) blind geothermal systems in southeastern Gabbs Valley by employing a geothermal play fairway analysis approach involving integration of geologic, geophysical, and geochemical datasets. Gabbs Valley is a complex, tectonically active basin within the Great Basin on the boundary between the transtensional central Walker Lane domain and extensional Basin and Range province. The termination of the Petrified Springs fault, a major dextral fault of the central Walker Lane, in an array of normal faults indicates that southeastern Gabbs Valley occupies a displacement transfer zone, which is a favorable structural setting for geothermal activity. The displacement transfer zone is a structurally complicated area with many faults of varying geometries, kinematics, and ages. A substantial northwest-trending gravity high within the south-central part of southeastern Gabbs Valley is produced from offsets along concealed northwest-striking dextral-normal faults that intersect strands of north-northeast-to east-northeast-striking normal faults. This area of tightly spaced faults and fault intersections is a particularly favorable structural setting within the broader-scale displacement transfer zone. Multiple lines of direct and indirect evidence suggest the presence of a relatively high-temperature (≥130°C) blind geothermal system in this area, including collocated intersecting gravity gradients, magnetic-low, low-resistivity, and 2-m temperature anomaly. Potentially related, water samples from agricultural wells ~7 km northwest of the 2-m anomaly yield geothermometers indicating subsurface fluid temperatures of 130-140°C. Six temperature-gradient holes were drilled to target the extent of the shallow-temperature and geophysical anomalies to define the geothermal system in southeastern Gabbs Valley. Two wells contained high temperatures exceeding boiling with bottom-hole temperatures ii of 114.5°C and 124.9°C, respectively, and the remaining wells displayed elevated to background temperatures ranging from 79.2°C to 28.7°C. The observed temperature gradients for the two hottest drill holes necessitate drill-hole intercepts of convecting hydrothermal fluids. The 2-m survey is spatially and thermally consistent with the temperature-gradient holes and constrains the core of upflow at a shallow depth interval (<250m) to an area <0.5-1 km west-southwest of the hottest drill hole. The zone of inferred upwelling occupies the area corresponding to the center of the magnetic-low. This project has several significant implications. The newly discovered blind geothermal system in southeastern Gabbs Valley may be capable of supporting a binary-type geothermal power plant. This detailed study provides an initial validation of the play fairway approach to geothermal exploration. It also demonstrates both the broad applicability of the play fairway exploration strategy and the large untapped potential for commercial-grade blind geothermal systems in many of the basins in the Great Basin region.

15 GEOTHERMAL ENERGY↗

DOE OSTI2016

Summary of Phase 1 and Plans for Phase 2 of the Washington State Geothermal Play-Fairway Analysis

A geothermal play fairway is constituted by the collocation of heat, permeability, and saturated porosity in sufficient volume that provide adequate heat exchange at depths accessible by modern drilling technologies. The focus of Phase 1 of the Washington State play-fairway analysis (PFA) was to develop and apply a methodology to integrate detailed models of heat and permeability potential for each play (study area). The goal is to minimize risk associated with investments in geothermal exploration of greenfield projects by rigorously analyzing available data, applying favorability modeling concepts that have been successfully used at known geothermal systems, and addressing data quality and model sensitivity. The integrated results indicate locations where commercial geothermal potential is highest, based on available data. Here, these results also reveal where new data are needed to increase resolution and reduce uncertainty to minimize risk (work conducted in Phase 2), and thus successfully identify sites for exploration wells (in Phase 3).

geothermal favorability↗

DOE OSTI2013

An angularly refineable phase space finite element method with approximate sweeping procedure

An angularly refineable phase space finite element method is proposed to solve the neutron transport equation. The method combines the advantages of two recently published schemes. The angular domain is discretized into small patches and patch-wise discontinuous angular basis functions are restricted to these patches, i.e. there is no overlap between basis functions corresponding to different patches. This approach yields block diagonal Jacobians with small block size and retains the possibility for S{sub n}-like approximate sweeping of the spatially discontinuous elements in order to provide efficient preconditioners for the solution procedure. On the other hand, the preservation of the full FEM framework (as opposed to collocation into a high-order S{sub n} scheme) retains the possibility of the Galerkin interpolated connection between phase space elements at arbitrary levels of discretization. Since the basis vectors are not orthonormal, a generalization of the Riemann procedure is introduced to separate the incoming and outgoing contributions in case of unstructured meshes. However, due to the properties of the angular discretization, the Riemann procedure can be avoided at a large fraction of the faces and this fraction rapidly increases as the level of refinement increases, contributing to the computational efficiency. In this paper the properties of the discretization scheme are studied with uniform refinement using an iterative solver based on the S{sub 2} sweep order of the spatial elements. The fourth order convergence of the scalar flux is shown as anticipated from earlier schemes and the rapidly decreasing fraction of required Riemann faces is illustrated. (authors)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Potential synergy: the thorium fuel cycle and rare earths processing

The use of thorium in nuclear power programs has been evaluated on a recurring basis. A concern often raised is the lack of 'thorium infrastructure'; however, for at least a part of a potential thorium fuel cycle, this may less of a problem than previously thought. Thorium is frequently encountered in association with rare earth elements and, since the U.S. last systematically evaluated the large-scale use of thorium (the 1970's,) the use of rare earth elements has increased ten-fold to approximately 200,000 metric tons per year. Integration of thorium extraction with rare earth processing has been previously described and top-level estimates have been done on thorium resource availability; however, since ores and mining operations differ markedly, what is needed is process flowsheet analysis to determine whether a specific mining operation can feasibly produce thorium as a by-product. Also, the collocation of thorium with rare earths means that, even if a thorium product stream is not developed, its presence in mining waste streams needs to be addressed and there are previous instances where this has caused issues. This study analyzes several operational mines, estimates the mines' ability to produce a thorium by-product stream, and discusses some waste management implications of recovering thorium. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2013

Coupling fuel cycles with repositories: how repository institutional choices may impact fuel cycle design

The historical repository siting strategy in the United States has been a top-down approach driven by federal government decision making but it has been a failure. This policy has led to dispatching fuel cycle facilities in different states. The U.S. government is now considering an alternative repository siting strategy based on voluntary agreements with state governments. If that occurs, state governments become key decision makers. They have different priorities. Those priorities may change the characteristics of the repository and the fuel cycle. State government priorities, when considering hosting a repository, are safety, financial incentives and jobs. It follows that states will demand that a repository be the center of the back end of the fuel cycle as a condition of hosting it. For example, states will push for collocation of transportation services, safeguards training, and navy/private SNF (Spent Nuclear Fuel) inspection at the repository site. Such activities would more than double local employment relative to what was planned for the Yucca Mountain-type repository. States may demand (1) the right to take future title of the SNF so if recycle became economic the reprocessing plant would be built at the repository site and (2) the right of a certain fraction of the repository capacity for foreign SNF. That would open the future option of leasing of fuel to foreign utilities with disposal of the SNF in the repository but with the state-government condition that the front-end fuel-cycle enrichment and fuel fabrication facilities be located in that state.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗