Anthony Scopatz

Carrier dynamics in self-assembled ErAs nanoislands embedded in GaAs measured by optical-pump terahertz-probe spectroscopy

We use optical-pump terahertz (THz)-probe spectroscopy to study carrier dynamics in self-assembled ErAs nanoislands embedded in GaAs and deposited in a superlattice structure. Measurements are performed at several pump fluences on samples with different superlattice periods, enabling a determination of the time-dependent conductivity. Subpicosecond carrier capture times are obtained, indicating the potential of these devices as time-domain THz detectors with performance comparable to low-temperature grown GaAs and superior control of material parameters.

Terahertz circular dichroism spectroscopy of biomolecules

Biopolymers such as proteins, DNA and RNA fold into large, macromolecular chiral structures. As charged macromolecules, they absorb strongly in the terahertz due to large-scale collective vibrational modes; as chiral objects, this absorption should be coupled with significant circular dichroism. Terahertz circular dichroism (TCD) is potentially important as a biospecific sensor, unobscured by spectral features related to abiological material. We have constructed atomistic simulations and elastic continuum models of TCD. These models estimate the magnitude of the TCD and the relation between TCD spectroscopic signatures (zero crossings) and the structure, charge distribution and mechanical properties of biomaterials. A broad band TCD spectrometer based on a polarizing interferometer is developed to explore TCD in biomolecules in aqueous solution. Preliminary results on TCD in lysozyme in water at several terahertz frequencies is presented.

Fundamental concepts in the Cyclus nuclear fuel cycle simulation framework

Nuclear fuel cycle modeling generality and robustness are improved by a modular, agent based modeling framework.Discrete material and facility tracking rather than fleet-based modeling improve nuclear fuel cycle simulation fidelity.A free, open source paradigm encourages technical experts to contribute software to the Cyclus modeling ecosystem.The flexibility of the Cyclus tool from the simulator user perspective is demonstrated with both open and closed fuel cycle examples. As nuclear power expands, technical, economic, political, and environmental analyses of nuclear fuel cycles by simulators increase in importance. To date, however, current tools are often fleet-based rather than discrete and restrictively licensed rather than open source. Each of these choices presents a challenge to modeling fidelity, generality, efficiency, robustness, and scientific transparency. The Cyclus nuclear fuel cycle simulator framework and its modeling ecosystem incorporate modern insights from simulation science and software architecture to solve these problems so that challenges in nuclear fuel cycle analysis can be better addressed. A summary of the Cyclus fuel cycle simulator framework and its modeling ecosystem are presented. Additionally, the implementation of each is discussed in the context of motivating challenges in nuclear fuel cycle simulation. Finally, the current capabilities of Cyclus are demonstrated for both open and closed fuel cycles.

Nonjudgmental Dynamic Fuel Cycle Benchmarking

Abstract This paper presents a new fuel cycle benchmarking analysis methodology by coupling Gaussian process (GP) regression, a popular technique in machine learning, to dynamic time warping, a mechanism widely used in speech recognition. Together, they generate figures of merit (FOMs) for a suite of fuel cycle realizations. The FOMs may be computed for any time series metric that is of interest to a benchmark. For a given metric, these FOMs have the advantage that they reduce the dimensionality to a scalar and are thus directly comparable. The FOMs account for uncertainty in the metric itself, utilize information across the whole time domain, and do not require that the simulators use a common time grid. Here, a distance measure is defined that can be used to compare the performance of each simulator for a given metric. Additionally, a contribution measure is derived from the distance measure that can be used to rank order the impact of different partitions of a fuel cycle metric. Lastly, this paper warns against using standard signal-processing techniques for error reduction, as error reduction is better handled by the GP regression itself.This paper presents a new fuel cycle benchmarking analysis methodology by coupling Gaussian process regression, a popular technique in Machine Learning, to dynamic time warping, a mechanism widely used in speech recognition. Together they generate figures-of-merit that are applicable to any time series metric that a benchmark may study. The figures-of-merit account for uncertainty in the metric itself, utilize information across the whole time domain, and do not require that the simulators use a common time grid. Here, a distance measure is defined that can be used to compare the performance of each simulator for a given metric. Additionally, a contribution measure is derived from the distance measure that can be used to rank order the importance of fuel cycle metrics. Lastly, this paper warns against using standard signal processing techniques for error reduction. This is because it is found that error reduction is better handled by the Gaussian process regression itself.

Facility Deployment Decisions Through Warp Optimization of Regressed Gaussian Processes

Abstract A method for quickly determining deployment schedules that meet any given fuel cycle demands is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of deployed facilities. Each of these predictions is measured against the demand curve using the dynamic time warping distance. The minimum-distance deployment schedule is evaluated in a full fuel cycle simulation, and the generated production curve then informs the model on the next optimization iteration. The method converges within five to ten iterations to a distance that is less than 1% of the total deployable production. This speed of convergence makes it suitable for use even when fuel cycle realizations are expensive, as in higher-fidelity or agent-based simulators. A representative once-through fuel cycle is used to demonstrate the methodology for reactor deployment. However, the algorithm itself is multivariate and may be used to determine the deployment schedules of many facility types that meet a number of independent criteria simultaneously. The once-through, electricity production example was chosen for the simplicity of illustrating the method.

Developing the User Experience for a Next Generation Nuclear Fuel Cycle Simulator (NGFCS)

With its recent roadmap for research, development and deployment (RD&D), the US Department of Energy’s Office of Nuclear Energy (DOE-NE) seeks to ensure that nuclear energy continues to be a competitive energy option for decades to come. Development of sustainable fuel cycles has been identified as a primary challenge. Continued research in technologies that support such fuel cycles will be conducted in the coming decades in order to support an ultimate decision for the path forward. RD&D decisions for individual nuclear energy technologies must be informed by the technical, political and socio-economic impacts of those technologies on the whole nuclear energy system. Therefore, the Fuel Cycle Research & Development program is developing a next generation fuel cycle simulator (NGFCS) with sufficient modularity to accommodate a wide variety of audiences, use cases and developer needs. The NGFCS is expected to be a useful evaluation tool for a variety of audiences. Non-technical audiences will interact with the NGFCS in a way that allows them to express critical high-level decisions and understand the outcomes of those decisions in a set of key metrics. Expert audiences may be interested in a quantitative technology assessment to help motivate design improvements. Developers of the NGFCS will need to visualize their results to ensure consistency and correctness. At the same time, it is important that the NGFCS allow experts to introduce new modules to capture specific physical models or market behavior, but rely on a common infrastructure that facilitates direct comparisons of results. This university team will focus on the design and development of the user experience for a next generation nuclear fuel cycle simulator (NGFCS), based on the software requirements and design developed in conjunction with the Fuel Cycles Technologies Systems Analysis Campaign. In addition to ensuring an adequate user environment for developers, including input generation and detailed quantitative output visualization, particular attention will be paid to the policyand decision-maker audience that may be interested in trends and trade-offs in a more qualitative manner. An interdisciplinary team will combine research in social science, computer science and nuclear engineering to support an innovative interface for nuclear fuel cycle systems analysis. This project will have five thrusts, conducted in parallel and integrated into a unified effort:  Identification of priority stakeholders, input parameters and output metrics  User interface for model generation using input parameters  Data translation to derive output metrics  Visualization environment for analytic reasoning and data exploration  Efficient Design of Client-Server Model

An Integrated Fuel Depletion Calculator for Fuel Cycle Options Analysis

Nuclear fuel cycle options analysis relies upon the ability to explore outcomes tied to the viability of fuel cycle strategies from the standpoints of economics, safety, security and/or sustainability in resource utilization and waste management. Specification of the charge and discharge isotopic compositions of reactor fuel offers an example of the challenges inherent in fuel cycle simulation. Existing models for evaluating fuel cycle options define sets of charge/discharge composition vectors, here called ‘recipes.’ Even for a simple analysis consisting of one reactor type, a large number of recipes would be needed to cover input/output compositions for plausible values of initial enrichment and fuel geometry parameters.. This work shifts the parameterization of reactor modeling for options analysis away from recipes and toward microscopic few energy-group cross sections as functions of burn time and other attributes that shape the neutron energy spectrum. This reactor model interpolates cross sections from pre-computed libraries to determine run-time values for a given state of the core. State-specific cross sections would then be used to calculate the neutron flux spectrum, multiplication factor, fuel burnup, and fuel vector at desired burn times. The research challenge lies in embedding the essential physics governing fuel burnup, the neutron energy spectrum, into the group cross sections in a rigorous but extensible fashion. A secondary challenge arises in formulating a multivariate interpolation algorithm to obtain each cross section from ‘nearby’ (in the phase space of configurations and initial isotopic compositions) multigroup libraries. Upon completion, the model would be accessible to all levels of potential users. This will be able to be done from a top-level rich client or web-based interface, by scripting the code itself, or by calling the new pipeline from within another fuel cycle application. Further justification for this model is that interpolation of the cross section enables much larger initial parameter sweeps covering a wider array of reactor states. Moreover by collectively varying all inputs of interest, the sensitivity and uncertainty of the reactor parameters may be measured.

ENTROPY-BASED NUCLEAR FUEL CYCLE SENSITIVITIES AND COVARIANCES

Technology development and deployment decisions are justified by weighing their costs against the expected benefits. Multiple nuclear fuel cycle (NFC) simulation models have been devised, some with the aim of quantifying cyclewide sensitivities to variations from base-case scenarios. Base-case sensitivity studies often perturb only one parameter at a time and only in the region around the initial value. This paper details a sensitivity study methodology that applies entropy-based statistical methods of information theory to describe outcomes produced by an NFC model. This supersedes past efforts at sensitivity and uncertainty analysis by allowing a much larger space to be explored. Here, 30 independent fuel cycle parameters for a fast reactor-light water reactor hybrid scenario are varied simultaneously and stochastically. This fuel cycle schema was chosen as a well-known, sufficiently complex model; the underlying statistical methods could be applied to any cycle. This study uses the uncertainty coefficient computed from contingency tables (CTs) to represent the sensitivity of a technology-defining input to the response. The response of interest here was taken to be the deep geologic repository capacity for a given realization of fuel cycle inputs. After computing the uncertainty coefficients, the inputs themselves are sorted based on decreasing sensitivities. Fast reactor used fuel plutonium separations were found to be most important to the cycle. Furthermore, to represent input covariances (the effect of one input on the sensitivity of a second input to the response), a new measure is defined on three-dimensional CTs. This metric is the coefficient of the variation of uncertainty coefficient of two-dimensional slices of the original table. Sorting by this sensitivity of sensitivity metric, the input pair of fast reactor americium separations together with high-level-waste storage time was found to have the largest joint effect on the repository capacity.

Carrier dynamics in self-assembled ErAs nanoislands measured by optical-pump THz-probe spectroscopy

We use optical-pump THz-probe spectroscopy to study carrier dynamics in self-assembled ErAs:GaAs nanoislands. Sub-picosecond carrier capture times are measured, indicating the potential of these devices as THz detectors comparable to low temperature grown GaAs.