Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Molybdenum-99 (Moly 99) is a critical raw material for Technetium (Tc) 99m, a radioactive isotope most widely used in nuclear medicine procedures. Moly 99 has a short half-life of about six hours, which means it cannot be stockpiled. When Moly 99 decays, it turns into Tc 99m, which has a half-life of 214,000 years. The photon energy emitted from the decay of Moly 99 is used in a variety of nuclear imaging technologies such as gamma cameras. Furthermore, radiopharmaceutical manufacturers use the photon energy emitted from the decay of Moly 99 to produce generators for hospitals, clinics, and radiopharmacies. Once Moly 99 decays to Tc 99m, it is used to make individual patient doses for a variety of diagnostic imaging procedures. The Moly 99 Reactor Design is a conceptual blueprint for Moly 99 production that does not use weapon-grade uranium; instead, the reactor has a target core of low-enriched uranium. The reactor design is small, reaching a foot-and-a-half in height and diameter and consumes less than two megawatts of power. The reactor's only purpose is for medical isotope production and with every fission, Moly 99 is produced.

Estimation of radionuclide aerosol release to the environment, from fire accident scenarios, are one of the most dominant accident evaluations at the U.S. Department of Energy's (DOE's) nuclear facilities. Of particular interest to safety analysts, is estimating the radionuclide aerosol release, the Source Term (ST), based on aerosol transport from a fire room to a corridor and from the corridor to the environment. However, no existing literature has been found on estimating ST from this multi-room facility configuration. This paper contributes the following to aerosol transport modeling body of work: a validation study on a multiroom fire experiment (this includes a code-to-code comparison between MELCOR and Consolidated Fire and Smoke Transport, a specialized fire code without radionuclide transport capabilities), a sensitivity study to provide insight on the effect of smoke on ST, and a sensitivity study on the effect of aerosol entrainment in the atmosphere (puff and continuous rate) on ST.

The work presented in this paper applies the MELCOR code developed at Sandia National Laboratories to evaluate the source terms from potential accidents in non-reactor nuclear facilities. The present approach provides an integrated source term approach that would be well-suited for uncertainty analysis and probabilistic risk assessments. MELCOR is used to predict the thermal-hydraulic conditions during fires or explosions that includes a release of radionuclides. The radionuclides are tracked throughout the facility from the initiating event to predict the time-dependent source term to the environment for subsequent dose or consequence evaluations. In this paper, we discuss the MELCOR input model development and the evaluation of the potential source terms from the dominated fire and explosion scenarios for a spent fuel nuclear reprocessing plant.

Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective (MO) extension, called srMOBO-3GP, to solve the MO optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GP is assigned with a different task: the first GP is used to approximate a single-objective computed from the MO definition, the second GP is used to learn the unknown constraints, and the third GP is used to learn the uncertain Pareto frontier. At each iteration, a MO augmented Tchebycheff function converting MO to single-objective is adopted and extended with a regularized ridge term, where the regularization is introduced to smooth the single-objective function. Finally, we couple the third GP along with the classical BO framework to explore the richness and diversity of the Pareto frontier by the exploitation and exploration acquisition function. The proposed framework is demonstrated using several numerical benchmark functions, as well as a thermomechanical finite element model for flip-chip package design optimization.

Wear prediction is important in designing reliable machinery for slurry industry. It usually relies on multi-phase computational fluid dynamics, which is accurate but computationally expensive. Each run of the simulations can take hours or days even on a high-performance computing platform. The high computational cost prohibits a large number of simulations in the process of design optimization. In contrast to physics-based simulations, data-driven approaches such as machine learning are capable of providing accurate wear predictions at a small fraction of computational costs, if the models are trained properly. In this paper, a recently developed WearGP framework [1] is extended to predict the global wear quantities of interest by constructing Gaussian process surrogates. The effects of different operating conditions are investigated. The advantages of the WearGP framework are demonstrated by its high accuracy and low computational cost in predicting wear rates.

Recent advances in horizontal cask designs for commercial spent nuclear fuel have significantly increased maximum thermal loading. This is due in part to greater efficiency in internal conduction pathways. Carefully measured data sets generated from testing of full-sized casks or smaller cask analogs are widely recognized as vital for validating thermal-hydraulic models of these storage cask designs. While several testing programs have been previously conducted, these earlier validation studies did not integrate all the physics or components important in a modern, horizontal dry cask system. The purpose of this investigation is to produce data sets that can be used to benchmark the codes and best practices presently used to calculate cladding temperatures and induced cooling air flows in modern, horizontal dry storage systems. The horizontal dry cask simulator (HDCS) has been designed to generate this benchmark data and complement the existing knowledge base. Transverse and axial temperature profiles along with induced-cooling air flow are measured using various backfills of gases for a wide range of decay powers and canister pressures. The data from the HDCS tests will be used to host a blind model validation effort.

Estimation of the uncertainty in a critical experiment attributable to uncertainties in the measured experiment temperature is done by calculating the variation of the eigenvalue of a benchmark configuration as a function of temperature. In the low-enriched water-moderated critical experiments performed at Sandia, this is done by 1) estimating the effects of changing the water temperature while holding the UO₂ fuel temperature constant, 2) estimating the effects of changing the UO₂ temperature while holding the water temperature constant, and 3) combining the two results. This assumes that the two effects are separable. The results of such an analysis are nonintuitive and need experimental verification. Critical experiments are being planned at Sandia National Laboratories (Sandia) to measure the effect of temperature on critical systems and will serve to test the methods used in estimating the temperature effects in critical experiments.

Although popular in industry, state-chart notations with ‘run to completion’ semantics lack formal refinement and rigorous verification methods. State-chart models are typically used to design complex control systems that respond to environmental triggers with a sequential process. The model is usually constructed at a concrete level and verified and validated using animation techniques relying on human judgement. Event-B, on the other hand, is based on refinement from an initial abstraction and is designed to make formal verification by automatic theorem provers feasible. We introduce a notion of refinement into a ‘run to completion’ statechart modelling notation, and leverage Event-B ’s tool support for theorem proving. We describe the difficulties in translating ‘run to completion’ semantics into Event-B refinements and suggest a solution. We illustrate our approach and show how critical (e.g. safety) invariant properties can be verified by proof despite the reactive nature of the system. We also show how behavioural aspects of the system can be verified by testing the expected reactions using a temporal logic model checking approach.

Heterogeneous Integration (HI) may enable optoelectronic transceivers for short-range and long-range radio frequency (RF) photonic interconnect using wavelength-division multiplexing (WDM) to aggregate signals, provide galvanic isolation, and reduce crosstalk and interference. Integration of silicon Complementary Metal-Oxide-Semiconductor (CMOS) electronics with InGaAsP compound semiconductor photonics provides the potential for high-performance microsystems that combine complex electronic functions with optoelectronic capabilities from rich bandgap engineering opportunities, and intimate integration allows short interconnects for lower power and latency. The dominant pure-play foundry model plus the differences in materials and processes between these technologies dictate separate fabrication of the devices followed by integration of individual die, presenting unique challenges in die preparation, metallization, and bumping, especially as interconnect densities increase. In this paper, we describe progress towards realizing an S-band WDM RF photonic link combining 180 nm silicon CMOS electronics with InGaAsP integrated optoelectronics, using HI processes and approaches that scale into microwave and millimeter-wave frequencies.

Estimation of the uncertainty in a critical experiment attributable to uncertainties in the measured experiment temperature is done by calculating the variation of the eigenvalue of a benchmark configuration as a function of temperature. In the low-enriched water-moderated critical experiments performed at Sandia, this is done by 1) estimating the effects of changing the water temperature while holding the UO₂ fuel temperature constant, 2) estimating the effects of changing the UO₂ temperature while holding the water temperature constant, and 3) combining the two results. This assumes that the two effects are separable. The results of such an analysis are nonintuitive and need experimental verification. Critical experiments are being planned at Sandia National Laboratories (Sandia) to measure the effect of temperature on critical systems and will serve to test the methods used in estimating the temperature effects in critical experiments.