Modeling shallow marine carbonate depositional systems

Geological Process Models (GPMs) have been used in the past to simulate the distinctive stratigraphies formed in carbonate sediments, and to explore the interaction of controls that produce heterogeneity. Previous GPMs have only indirectly included the supersaturation of calcium carbonate in seawater, a key physicochemical control on carbonate production in reef and lagoon environments, by modifying production rates based on the distance from open marine sources. We here use the residence time of water in the lagoon and reef areas as a proxy for the supersaturation state of carbonate in a new process model, Carbonate GPM. Residence times in the model are calculated using a particle-tracking algorithm. Carbonate production is also controlled by water depth and wave power dissipation. Once deposited, sediment can be eroded, transported and re-deposited via both advective and diffusive processes. We show that using residence time as a control on production might explain the formation of non-ordered, three-dimensional carbonate stratigraphies by lateral shifts in the locus of carbonate deposition on timescales comparable to so-called 5th-order sea-level oscillations. We also show that representing supersaturation as a function of distance from open marine sources, as in previous models, cannot correctly predict the supersaturation distribution over a lagoon due to the intricacies of the flow regime.     

Quantification of margins and uncertainties: Example analyses from reactor safety and radioactive waste disposal involving the separation of aleatory and epistemic uncertainty

In 2001, the National Nuclear Security Administration (NNSA) of the U.S. Department of Energy (DOE) in conjunction with the national security laboratories (i.e., Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Sandia National Laboratories) initiated development of a process designated quantification of margins and uncertainties (QMU) for the use of risk assessment methodologies in the certification of the reliability and safety of the nation's nuclear weapons stockpile. A previous presentation, “Quantification of Margins and Uncertainties: Conceptual and Computational Basis,” describes the basic ideas that underlie QMU and illustrates these ideas with two notional examples. The basic ideas and challenges that underlie NNSA's mandate for QMU are present, and have been successfully addressed, in a number of past analyses for complex systems. To provide perspective on the implementation of a requirement for QMU in the analysis of a complex system, three past analyses are presented as examples: (i) the probabilistic risk assessment carried out for the Surry Nuclear Power Station as part of the U.S. Nuclear Regulatory Commission's (NRC's) reassessment of the risk from commercial nuclear power in the United States (i.e., the NUREG-1150 study), (ii) the performance assessment for the Waste Isolation Pilot Plant carried out by the DOE in support of a successful compliance certification application to the U.S. Environmental Agency, and (iii) the performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada, carried out by the DOE in support of a license application to the NRC. Each of the preceding analyses involved a detailed treatment of uncertainty and produced results used to establish compliance with specific numerical requirements on the performance of the system under study. As a result, these studies illustrate the determination of both margins and the uncertainty in margins in real analyses.     

Computer Systems Based on Silicon Photonic Interconnects

We present a computing microsystem that uniquely leverages the bandwidth, density, and latency advantages of silicon photonic interconnect to enable highly compact supercomputer-scale systems. We describe and justify single-node and multinode systems interconnected with wavelength-routed optical links, quantify their benefits vis-a-vis electrically connected systems, analyze the constituent optical component and system requirements, and provide an overview of the critical technologies needed to fulfill this system vision. This vision calls for more than a hundredfold reduction in energy to communicate an optical bit of information. We explore the power dissipation of a photonic link, suggest a roadmap to lower the energy-per-bit of silicon photonic interconnects, and identify the challenges that will be faced by device and circuit designers towards this goal.     

Implications of sensor location in steam reformer temperature control

Hydrogen can be produced via steam reformation of many feedstocks. External heat sources provide the thermal energy required by the endothermic steam reformation reactions. Temperature control of the steam reformation reactor is critical to reactor performance and catalyst life. Closed-loop control systems are typically used to modulate the heat input rate based on a comparison between a set point temperature and a temperature measurement. The location of the temperature sensor relative to the heat input location is a choice made during reactor design that can have significant impact on reactor temperature control.     

Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models

The analysis of many physical and engineering problems involves running complex computational models (simulation models, computer codes). With problems of this type, it is important to understand the relationships between the input variables (whose values are often imprecisely known) and the output. The goal of sensitivity analysis (SA) is to study this relationship and identify the most significant factors or variables affecting the results of the model. In this presentation, an improvement on existing methods for SA of complex computer models is described for use when the model is too computationally expensive for a standard Monte-Carlo analysis. In these situations, a meta-model or surrogate model can be used to estimate the necessary sensitivity index for each input. A sensitivity index is a measure of the variance in the response that is due to the uncertainty in an input. Most existing approaches to this problem either do not work well with a large number of input variables and/or they ignore the error involved in estimating a sensitivity index. Here, a new approach to sensitivity index estimation using meta-models and bootstrap confidence intervals is described that provides solutions to these drawbacks. Further, an efficient yet effective approach to incorporate this methodology into an actual SA is presented. Several simulated and real examples illustrate the utility of this approach. This framework can be extended to uncertainty analysis as well.