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Zhou W.,Rensselaer Polytechnic Institute | Apted M.J.,Intera Inc. | Kessler J.H.,EPRI
Nuclear Technology | Year: 2010

This paper describes the recent work to evaluate the technical storage capacity for spent fuel in the Yucca Mountain repository. To increase the capacity from the current statutory limit of 63000 tonnes HM commercial spent nuclear fuel (CSNF), two alternative repository designs are proposed and analyzed, which add two additional emplacement drifts adjacent to each currentdesign drift. All designs assume the same waste package inventory, or heat generation rate, and drift ventilation as the current design. As both alternative designs would fit the well-characterized repository footprint, no additional site characterization at Yucca Mountain would be necessary. The work also examines extended ventilation and phased waste-loading assumptions in anticipation of an expanded role for nuclear power in electricity generation. The key parameter to the storage capacity in the Yucca Mountain site is water movement. To study the thermal and hydrological responses to increased storage capacity, series of two-dimensional models were used to simulate coupled heat and mass (water and air) transfer within the repository system and the near-field subsurface environment, including all geological formations above and below the repository horizon from the surface to the water table. A three-dimensional model was applied to investigate the effect of axial heat transfer and fluid flow. The results show that the current repository footprint can accommodate three times the currently legislated 63000 tonnes HM of CSNF without compromising repository performance. Source

Clemo T.,Boise State University | Clemo T.,Intera Inc.
Ground Water | Year: 2010

A model coupling fluid hydraulics in a borehole with fluid flow in an aquifer is developed in this paper. Conservation of momentum is used to create a one-dimensional steady-state model of vertical flow in an open borehole combined with radially symmetric flow in an aquifer and with inflow to the well through the wellbore screen. Both laminar and turbulent wellbore conditions are treated. The influence of inflow through the wellbore screen on vertical flow in the wellbore is included, using a relation developed by Siwoń (1987). The influence of inflow reduces the predicted vertical variation in head up to 15% compared to a calculation of head losses due to fluid acceleration and the conventional Colebrook-White formulation of friction losses in a circular pipe. The wellbore flow model is embedded into the MODFLOW-2000 ground water flow code. The nonlinear conservation of momentum equations are iteratively linearized to calculate the conductance terms for vertical flow in the wellbore. The resulting simulations agree favorably with previously published results when the model is adjusted to meet the assumptions of the previous coupled models. © 2009 National Ground Water Association. Source

Hodges B.R.,University of Texas at Austin | Furnans J.E.,Intera Inc. | Kulis P.S.,University of Texas at Austin
Journal of Hydraulic Engineering | Year: 2011

Measurements of stratification and dissolved oxygen (DO) illustrate a hypersaline gravity current with salt loads similar to a desalination plant brine discharge. Over a 48-h sampling period in August 2005, alternating cycles of high- and low-temperature hypersaline water were observed along the bottom of Corpus Christi Bay in Texas, coincident with low benthic DO and tidal flushing from an adjacent smaller bay. The gravity current underflow was typically less than 10% of the overall water depth. Strong salinity gradients prevented wind-mixing of the entire water column. Hypoxic and near-hypoxic conditions were associated with limited DO replenishment from the ambient water. High DO levels in the underflow source water did not deter the development of offshore benthic hypoxia. A quasi-Lagrangian analysis is used to evaluate the relationship between ambient mixing and lateral mixing within the underflow. The analysis is further applied to estimating DO demand rates in the hypersaline plume. Mixing between the ambient water and the underflow predominately occurs over the sloping bay boundary. Once the gravity current reaches the flatter section of the bay, mixing is substantially reduced and DO is progressively depleted at the bottom. The transit time of the underflow (i.e., residence time or isolation time for water near the bottom) and wind-mixing energy appear to be key factors governing stratification persistence and potential hypoxia development. The observations and analyses provide insight into possible fate, impacts, and open questions associated with similarly scaled salt loadings from a desalination plant into a shallow bay. © 2011 American Society of Civil Engineers. Source

Singh A.,Intera Inc. | Walker D.D.,Illinois State Water Survey | Minsker B.S.,University of Illinois at Urbana - Champaign | Valocchi A.J.,University of Illinois at Urbana - Champaign
Stochastic Environmental Research and Risk Assessment | Year: 2010

The interactive multi-objective genetic algorithm (IMOGA) combines traditional optimization with an interactive framework that considers the subjective knowledge of hydro-geological experts in addition to quantitative calibration measures such as calibration errors and regularization to solve the groundwater inverse problem. The IMOGA is inherently a deterministic framework and identifies multiple large-scale parameter fields (typically head and transmissivity data are used to identify transmissivity fields). These large-scale parameter fields represent the optimal trade-offs between the different criteria (quantitative and qualitative) used in the IMOGA. This paper further extends the IMOGA to incorporate uncertainty both in the large-scale trends as well as the small-scale variability (which can not be resolved using the field data) in the parameter fields. The different parameter fields identified by the IMOGA represent the uncertainty in large-scale trends, and this uncertainty is modeled using a Bayesian approach where calibration error, regularization, and the expert's subjective preference are combined to compute a likelihood metric for each parameter field. Small-scale (stochastic) variability is modeled using a geostatistical approach and added onto the large-scale trends identified by the IMOGA. This approach is applied to the Waste Isolation Pilot Plant (WIPP) case-study. Results, with and without expert interaction, are analyzed and the impact that expert judgment has on predictive uncertainty at the WIPP site is discussed. It is shown that for this case, expert interaction leads to more conservative solutions as the expert compensates for some of the lack of data and modeling approximations introduced in the formulation of the problem. © 2010 Springer-Verlag. Source

Singh A.,Intera Inc. | Minsker B.S.,University of Illinois at Urbana - Champaign | Bajcsy P.,University of Illinois at Urbana - Champaign
Journal of Computing in Civil Engineering | Year: 2010

The interactive multiobjective genetic algorithm (IMOGA) is a promising new approach to calibrate models. The IMOGA combines traditional optimization with an interactive framework, thus allowing both quantitative calibration criteria as well as the subjective knowledge of experts to drive the search for model parameters. One of the major challenges in using such interactive systems is the burden they impose on the experts that interact with the system. This paper proposes the use of a novel image-based machine-learning (IBML) approach to reduce the number of user interactions required to identify promising calibration solutions involving spatially distributed parameter fields (e.g., hydraulic conductivity parameters in a groundwater model). The first step in the IBML approach involves selecting a few highly representative solutions for expert ranking. The selection is performed using unsupervised clustering approaches from the field of image processing, which group potential parameter fields based on their spatial similarities. The expert then ranks these representative solutions, after which a machine-learning model (augmented with the spatial information of the selected fields) is trained to learn user preferences and predict rankings for solutions not ranked by the expert. To better mimic the "visual" information processing of human experts, algorithms from the field of image processing are used to mine information about the spatial characteristics of parameter fields, thus improving the performance of the clustering and machine-learning algorithms. The IBML approach is tested and demonstrated on a groundwater calibration problem and is shown to lead to significant improvements, reducing the amount of user interaction by as much as half without compromising the solution quality of the IMOGA. © 2010 ASCE. Source

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