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DOE OSTI2021

Regulatory Treatment of Low Frequency External Events under a Risk-Informed Performance-Based Licensing Pathway

Significant efforts are underway to develop and implement a risk-informed performance-based licensing framework for advanced reactors built on the establishment of an affirmative safety case. Such approaches offer the benefits of increased flexibility regarding key design and licensing decisions based on a detailed assessment and understanding of plant risk. These methods include the comprehensive analysis of scenarios that have historically been addressed through conservative and deterministic licensing analysis approaches, such as the evaluation of event sequences at frequencies of occurrence beyond the design basis. While the detailed analysis of low frequency events provides insights that can help justify alternative treatments to past conservatism, the findings are dependent on the quality and confidence associated with the analyses. The assessment of external hazards presents a unique challenge, as their potential frequency of occurrence, especially of large magnitude events, is inherently uncertain given the long return periods in question. The current project seeks to further explore the regulatory treatment of low frequency external events as part of a risk-informed performance-based framework. The goals are to identify the benefits and challenges of such approaches for advanced reactor vendors and to aid in the development of consistent and appropriate methodologies. As part of this effort, the current report summarizes initial project findings, with a focus on the evaluation of the seismic hazard. This includes an exploration of the implementation of a risk-informed performance-based approach through an example analysis. Based on the identified challenges associated with the probabilistic treatment of low frequency event sequences, additional performance-based methods were explored as complementary tools for demonstrating regulatory compliance. The report highlights key challenges and opportunities associated with the methods and outlines recommendations and future activities to assist with their implementation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2017

WEC Extreme Conditions Modeling Sandia Summer Intern 2017 Report

This report contains work completed by a group of student interns during the summer of 2017. Under the guidance of Ryan Coe, Aubrey Eckert-Gallup, and Nevin Martin, a series of interrelated projects were completed on topics relating to extreme response and survival analysis of wave energy converters (WECs). Jarred Canning studied long-term design response analysis methods for WECs. Sam Edwards studied how variation in the selection of an environmental contour affects the characterization of WEC response in extreme conditions. Sam also led the integration of various components of this report and overall editing. Tyler Esterly produced a catalog of analyses for different ocean sites. Bibiana Seng studied clustering analyses for comparing the wave environments of different ocean sites. Lori Smith performed a comparison between analyses conducted using spectral wave data and analyses using deterministic time-domain wave data. William ("Zach") Stuart studied the sensitivity and convergence of environmental contour methods.

16 TIDAL AND WAVE POWER↗

DOE OSTI2015

Phase space reconstruction and estimation of the largest Lyapunov exponent for gait kinematic data

The authors describe an example of application of nonlinear time series analysis directed at identifying the presence of deterministic chaos in human motion data by means of the largest Lyapunov exponent. The method was previously verified on the basis of a time series constructed from the numerical solutions of both the Lorenz and the Rössler nonlinear dynamical systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Bayesian homodyne and heterodyne tomography

Continuous-variable (CV) photonic states are of increasing interest in quantum information science, bolstered by features such as deterministic resource state generation and error correction via bosonic codes. Data-efficient characterization methods will prove critical in the fine-tuning and maturation of such CV quantum technology. Although Bayesian inference offers appealing properties—including uncertainty quantification and optimality in mean-squared error—Bayesian methods have yet to be demonstrated for the tomography of arbitrary CV states. Here we introduce a complete Bayesian quantum state tomography workflow capable of inferring generic CV states measured by homodyne or heterodyne detection, with no assumption of Gaussianity. As examples, we demonstrate our approach on experimental coherent, thermal, and cat state data, obtaining excellent agreement between our Bayesian estimates and theoretical predictions. Our approach lays the groundwork for Bayesian estimation of highly complex CV quantum states in emerging quantum photonic platforms, such as quantum communications networks and sensors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

DECOVALEX-2023: Task F2-Salt Final Report

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

DECOVALEX-2023: Task F2 Salt Final Report

The subject of Task F of DECOVALEX-2023 concerns performance assessment modelling of radioactive waste disposal in deep mined repositories. The primary objectives of Task F are to build confidence in the models, methods, and software used for performance assessment (PA) of deep geologic nuclear waste repositories, and/or to bring to the fore additional research and development needed to improve PA methodologies. In Task F2- (salt), these objectives have been accomplished through staged development and comparison of the models and methods used by participating teams in their PA frameworks. Coupled-process submodels and deterministic simulations of the entire PA model for a reference scenario for waste disposal in domal salt have been conducted. The task specification has been updated continuously since the initiation of the project to reflect the staged development of the conceptual repository model and performance metrics. Thermal, hydrological, mechanical, and chemical properties of individual components of the engineered and natural system were chosen for relevance by participating teams. The salt reference case system was characterized using data and measurements collected at relevant underground research laboratories (URLs), field sites, and simulation results from teams with specialized modelling capability. Participating teams made a wide range of model assumptions from compartmentalized networks to full 3D models of the salt formation. No single contributed model includes full-fidelity representation of all the features, events, and processes (FEPs) detailed in the task specification, but almost all features and processes are represented in at least one model. Despite differences in the modelling strategies developed by participating teams, all models indicate that salt compaction and radionuclide diffusion are key processes in the repository, and for the FEPs and model scenario considered, little of the disposed radionuclides will migrate beyond the repository seal over the 100,000 year simulations. In general, the model output quantities have the largest differences over the short term and near the waste. The models tend to be more similar further from waste and at later time. Disparities between the models are believed to be due to differing simplifications from the task specification, some of which are chosen simplifications to reduce complexity, and some are restrictions imposed by the modelling tools. A second round of this task has been accepted for DECOVALEX-2027 in conjunction with Task F1 on crystalline PA modelling. The future round includes waste package heating, improved modelling of salt creep closure, additional comparisons of coupled-process sub-models, and the impact of repository engineering design on radionuclide migration in the repository. Participants will also propose and finalize a set of uncertain inputs for the reference case simulations, propagate these uncertainties in a set of realizations, and conduct sensitivity analyses on the simulation results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

A Deep Reinforcement Learning-based Reserve Optimization in Active Distribution Systems for Tertiary Frequency Regulation

Federal Energy Regulatory Commission (FERC)Orders 841 and 2222 have recommended that distributed energy resources (DERs) should participate in energy and reserve markets; therefore, a mechanism needs to be developed to facilitate DERs’ participation at the distribution level. Although the available reserve from a single distribution system may not be sufficient for tertiary frequency regulation, stacked and coordinated contributions from several distribution systems can enable them participate in tertiary frequency regulation at scale. This paper proposes a deep reinforcement learning (DRL)-based approach for optimization of requested aggregated reserves by system operators among the clusters of DERs. The co-optimization of cost of reserve, distribution network loss, and voltage regulation of the feeders are considered while optimizing the reserves among participating DERs. The proposed framework adopts deep deterministic policy gradient (DDPG), which is an algorithm based on an actor-critic method. The effectiveness of the proposed method for allocating reserves among DERs is demonstrated through case studies on a modified IEEE 34-node distribution system.

deep reinforcement learning, distributed energy re↗

DOE OSTI2022

Learning Stochastic Parametric Differentiable Predictive Control Policies

We present a scalable unsupervised learning-based method for obtaining explicit control policies for model predictive control problems for stochastic linear systems with additive uncertainties subject to nonlinear chance constraints. We call the proposed method stochastic parametric differentiable predictive control (SP-DPC), which extends the recently proposed deterministic DPC policy optimization algorithm. We formulate the SP-DPC as a deterministic approximation to the stochastic parametric constrained optimal control problem via independent sampling of the problem's parameters and uncertainties. This formulation allows us to directly compute the policy gradients via automatic differentiation of the problem's value function, evaluated over sampled parameters and uncertainties. In particular, the computed expectation of the problem's value function is backpropagated through the finite-time closed-loop system rollouts parametrized by a known nominal system dynamics model and neural control policy. We also provide theoretical probabilistic guarantees on closed-loop stability and chance constraints satisfaction for systems controlled by learned neural policies. We demonstrate the computational efficiency and scalability of the proposed policy optimization algorithm in three numerical examples, including systems with a large number of states or subject to nonlinear constraints.

Drgona, Jan↗

DOE OSTI2022

Comparisons of Nodal diffusion and whole-core transport methods for multiple cycles of a small Light water reacto

As part of a cooperative research and development agreement between the US department of Energy's Oak Ridge National Laboratory and Holtec International subsidiary SMR LLC, core simulator models of the SMR-160, a light water-cooled and -moderated, natural circulation, small modular reactor, were developed and compared. The model results indicate pin-by-pin power histories, critical boron concentration, and other key performance indicators useful for developing multi-cycle core loading patterns. One model uses industry standard nodal methods with a simple thermal-hydraulics solution, and the other uses an advanced deterministic neutronics solver with a coupled subchannel calculation. The goals of the comparison are to provide the reactor designer with a detailed and coupled core physics reference solution, inform the reactor designer of nodal model best practices based on the code-to-code comparison, and set up a workflow for performing subsequent detailed core simulator calculations. The results of this work will be used in the development of coupled neutronic, thermal hydraulic, and fuel performance models for advanced analysis of the SMR-160. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

First principles prediction of amorphous phases using evolutionary algorithms

We discuss the efficacy of evolutionary method for the purpose of structural analysis of amorphous solids. At present, ab initio molecular dynamics (MD) based melt-quench technique is used and this deterministic approach has proven to be successful to study amorphous materials. We show that a stochastic approach motivated by Darwinian evolution can also be used to simulate amorphous structures. Applying this method, in conjunction with density functional theory based electronic, ionic and cell relaxation, we re-investigate two well known amorphous semiconductors, namely silicon and indium gallium zinc oxide. We find that characteristic structural parameters like average bond length and bond angle are within ∼2% of those reported by ab initio MD calculations and experimental studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Motivation for Using Generalized Geometry in the Time Dependent Transport Code TDKENO

We are proposing to use the code, TDKENO, to model TREAT. TDKENO solves the time dependent, three dimensional Boltzmann transport equation with explicit representation of delayed neutrons. Instead of directly integrating this equation, the neutron flux is factored into two components – a rapidly varying amplitude equation and a slowly varying shape equation and each is solved separately on different time scales. The shape equation is solved using the 3D Monte Carlo transport code KENO, from Oak Ridge National Laboratory’s SCALE code package. Using the Monte Carlo method to solve the shape equation is still computationally intensive, but the operation is only performed when needed. The amplitude equation is solved deterministically and frequently, so the solution gives an accurate time-dependent solution without having to repeatedly We have modified TDKENO to incorporate KENO-VI so that we may accurately represent the geometries within TREAT. This paper explains the motivation behind using generalized geometry, and provides the results of our modifications. TDKENO uses the Improved Quasi-Static method to accomplish this. In this method, the neutron flux is factored into two components. One component is a purely time-dependent and rapidly varying amplitude function, which is solved deterministically and very frequently (small time steps). The other is a slowly varying flux shape function that weakly depends on time and is only solved when needed (significantly larger time steps).

IQS↗

DOE OSTI2026

Automated Signal Timing Plan Reconstruction Using High-Resolution Event-Based Controller Data for Digital Twins

Transportation digital twins are essential tools for evaluating emerging technologies such as connected and automated vehicles, adaptive traffic signal control, and mobility optimization strategies. Realistic digital twins require accurate emulation of real-world signal controllers and detailed signal timing plans. However, signal timing plans are often unavailable or difficult to access, forcing researchers and modelers to rely on assumed fixed timings or halt their analysis. To overcome this challenge, we present a method that directly estimates signal timing plan parameters using high-resolution, event-based data from traffic signal controllers. The proposed method extracts key parameters, including cycle length, offset, phase sequence, coordinated phases, phase-specific minimum and maximum green durations, vehicle extensions, and splits under coordination. A rule-based deterministic signal timing reconstruction algorithm based on traffic signal operation rules, such as those outlined in the Signal Timing Manual, is developed and validated. We evaluate this method, which uses high-resolution controller event logs and verified signal timing plans, on 94 signalized intersections in Nashville, Tennessee, demonstrating their ability to generate accurate, simulation-ready signal timing plans for tools such as SUMO and Vissim.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗

DOE OSTI2015

The generation of arbitrary order, non-classical, Gauss-type quadrature for transport applications

A method is presented, based upon the Stieltjes method (1884), for the determination of non-classical Gauss-type quadrature rules, and the associated sets of abscissae and weights. The method is then used to generate a number of quadrature sets, to arbitrary order, which are primarily aimed at deterministic transport calculations. The quadrature rules and sets detailed include arbitrary order reproductions of those presented by Abu-Shumays in [4,8] (known as the QR sets, but labelled QRA here), in addition to a number of new rules and associated sets; these are generated in a similar way, and we label them the QRS quadrature sets. The method presented here shifts the inherent difficulty (encountered by Abu-Shumays) associated with solving the non-linear moment equations, particular to the required quadrature rule, to one of the determination of non-classical weight functions and the subsequent calculation of various associated inner products. Once a quadrature rule has been written in a standard form, with an associated weight function having been identified, the calculation of the required inner products is achieved using specific variable transformations, in addition to the use of rapid, highly accurate quadrature suited to this purpose. The associated non-classical Gauss quadrature sets can then be determined, and this can be done to any order very rapidly. In this paper, instead of listing weights and abscissae for the different quadrature sets detailed (of which there are a number), the MATLAB code written to generate them is included as Appendix D. The accuracy and efficacy (in a transport setting) of the quadrature sets presented is not tested in this paper (although the accuracy of the QRA quadrature sets has been studied in [12,13]), but comparisons to tabulated results listed in [8] are made. When comparisons are made with one of the azimuthal QRA sets detailed in [8], the inherent difficulty in the method of generation, used there, becomes apparent, with the highest order tabulated sets showing unexpected anomalies. Although not in an actual transport setting, the accuracy of the sets presented here is assessed to some extent, by using them to approximate integrals (over an octant of the unit sphere) of various high order spherical harmonics. When this is done, errors in the tabulated QRA sets present themselves at the highest tabulated orders, whilst combinations of the new QRS quadrature sets offer some improvements in accuracy over the original QRA sets. Finally, in order to offer a quick, visual understanding of the various quadrature sets presented, when combined to give product sets for the purposes of integrating functions confined to the surface of a sphere, three-dimensional representations of points located on an octant of the unit sphere (as in [8,12]) are shown.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Modeling and Optimization Methods for Controlling and Sizing Grid-connected Energy Storage: A Review

Purpose of Review Energy storage is capable of providing a variety of services and solving a multitude of issues in today's rapidly evolving electric power grid. This paper reviews recent research on modeling and optimization methods for optimally controlling and sizing grid-connected battery energy storage systems (BESSs). Open issues and promising research directions are discussed. Recent Findings Recent studies on BESS dispatch, evaluation, and sizing focus on advanced modeling and optimization methods to maximize stacked value streams from multiple services. BESS models have been improved to better represent operational characteristics or capture degradation effects. Different solution methods and optimization techniques have been proposed to improve the benefits and cost-effectiveness of BESSs, using deterministic approaches prevalently but with impressive progress in capturing and addressing uncertainties. Summary Recent progress in BESS scheduling and sizing better supports planning and operational decision-making in different use cases, which is highly important to advance the deployment of BESSs. Additional research is required to properly model the trade-off between short-term benefits and service life with multiple degradation effects explicitly considered in the decision-making process. Advanced methods are to be developed for effectively determining optimal BESS sizes that maximize overall benefits within a varying lifetime considering diversified system, conditions as well as uncertainties at planning and operational stages.

Wu, Di↗

DOE OSTI2017

Stochastic simulation of reaction-diffusion systems: A fluctuating-hydrodynamics approach

Here, we develop numerical methods for stochastic reaction-diffusion systems based on approaches used for fluctuating hydrodynamics (FHD). For hydrodynamic systems, the FHD formulation is formally described by stochastic partial differential equations (SPDEs). In the reaction-diffusion systems we consider, our model becomes similar to the reaction-diffusion master equation (RDME) description when our SPDEs are spatially discretized and reactions are modeled as a source term having Poisson fluctuations. However, unlike the RDME, which becomes prohibitively expensive for an increasing number of molecules, our FHD-based description naturally extends from the regime where fluctuations are strong, i.e., each mesoscopic cell has few (reactive) molecules, to regimes with moderate or weak fluctuations, and ultimately to the deterministic limit. By treating diffusion implicitly, we avoid the severe restriction on time step size that limits all methods based on explicit treatments of diffusion and construct numerical methods that are more efficient than RDME methods, without compromising accuracy. Guided by an analysis of the accuracy of the distribution of steady-state fluctuations for the linearized reaction-diffusion model, we construct several two-stage (predictor-corrector) schemes, where diffusion is treated using a stochastic Crank-Nicolson method, and reactions are handled by the stochastic simulation algorithm of Gillespie or a weakly second-order tau leaping method. We find that an implicit midpoint tau leaping scheme attains second-order weak accuracy in the linearized setting and gives an accurate and stable structure factor for a time step size of an order of magnitude larger than the hopping time scale of diffusing molecules. We study the numerical accuracy of our methods for the Schlögl reaction-diffusion model both in and out of thermodynamic equilibrium. We demonstrate and quantify the importance of thermodynamic fluctuations to the formation of a two-dimensional Turing-like pattern and examine the effect of fluctuations on three-dimensional chemical front propagation. Furthermore, by comparing stochastic simulations to deterministic reaction-diffusion simulations, we show that fluctuations accelerate pattern formation in spatially homogeneous systems and lead to a qualitatively different disordered pattern behind a traveling wave.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Switch Opening and Exchange Method for Stochastic Distribution Network Reconfiguration

The purpose of distribution network reconfiguration (DNR) is to determine the optimal topology of an electricity distribution network, which is an efficient measure to reduce network power losses. Electricity load demand and photovoltaic (PV) output are uncertain and vary with time of day, and will affect the optimal network topology. Single-hour deterministic DNR is incapable of handling this uncertainty and variability. Therefore, this paper proposes to solve a multi-hour stochastic DNR (SDNR). Existing solution methods for DNR are either inaccurate or excessively time-consuming, and are therefore incapable of solving multi-hour SDNRs for large distribution networks. In this regard, a switch opening and exchange (SOE) method is proposed. Starting from a looped network with all switches closed, the SOE consists of three steps. The first step is to sequentially open the switches until all of the loops are opened. The second and third steps modify the status of branches obtained in the first step to obtain better radial topologies. Five test systems are used to validate the accuracy and fast solution speed of the SOE and the superiority of multi-hour SDNR over single-hour deterministic DNR.

distribution network reconfiguration↗

DOE OSTI2023

Hand Calculation Methods for Nuclear Criticality Safety

This primer provides an overview of the most common hand calculation methods used for criticality safety calculations. The most widely used tools available to a nuclear criticality safety (NCS) practitioner are probably the common Monte Carlo or deterministic criticality safety codes, which can be used to model very complex systems. However, use of these codes can obscure the parameters to which a particular fissile system may be sensitive, whereas the hand calculation methods can be used to delve into the ways each parameter may affect the reactivity of a fissile material system. Furthermore, practitioners must avoid using computer codes as devices that take inputs and simply provide outputs (i.e., a “black box”). Many years ago, pioneers such as Joe Thomas, David Smith, and Hugh Paxton, among others in the field of nuclear criticality safety, took the time before the advent of high-speed desktop computers to create simple hand methods for criticality safety analyses. Some of the methods can be used for single fissile units; others are applicable to fissile units arranged into simple array configurations. This primer discusses the applicability of the various methods, illustrates how they are used, and provides an interpretation of the various results. The NCS practitioner will need to spend time to master the methods that could be most useful; however, they can provide the practitioner with fast and accurate answers to criticality safety problems if they are used correctly and if critical data exist for the problem at hand. Hand calculation methods can be used as a starting point for more advanced calculations, and in many circumstances, they can provide sensitivity and perturbation information more quickly than using a criticality code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Accuracy of predictions made by machine learned models for biocrude yields obtained from hydrothermal liquefaction of organic wastes

Hydrothermal liquefaction (HTL) has potential for converting abundant wet organic wastes into renewable fuels. Because HTL consists of a complex reaction network, deterministic, physics-based prediction of its biocrude yield is prohibitively difficult. Data-driven methods provide an alternative to the physics-based approach; however, rigorous testing must be performed to ensure the accuracy of predictions made by data-driven methods. To this end, a data set was assembled consisting of 570 data points appearing in the open literature. The data set was divided into training, validation, and test sub-sets and used for evaluating different machine learning regression approaches to predict biocrude yield. Among the tested algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost) predicted biocrude yields in a test set that had not been used for training with the greatest accuracy, with root mean square errors (RMSE) of 8.34 and 8.57, respectively. Further refinement of the Random Forest model reduced its RMSE to 8.07. In comparison, predictions of a series of literature models resulted in RMSE ranging from 9.16 in the most accurate case to 27.6 in the least accurate; most literature models yielded RMSE values > 10. Using biocrude yield predictions from the most accurate Random Forest model and a probabilistic economic analysis found that the model accuracy is sufficient to prioritize allocation of resources based on projected minimum fuel selling price. In our report the models and analysis represent a major advance in the ability to use readily available data to predict biocrude yields on new feedstocks that have not previously been studied.

42 ENGINEERING↗