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The development of computer models for a flexible production system is considered, as a step toward automated design. The formulation of modeling algorithms for flexible production systems at the level of technological operations is outlined. The corresponding software for a computerized modeling system is described.
At Lawrence Livermore National Laboratory (LLNL), we operate and maintain the Quantum Device and Integration Testbed (QuDIT) facility, a small quantum testbed that supports about 10 active research teams (including our own) and over 50 internal and external collaborators. This testbed is designed to give remote white box access to users for research, training, and outreach. A guiding principle behind the development of our testbed infrastructure, software and user interfaces is to empower users to perform experiments at the cutting edge of quantum information science at any level of abstraction, from materials studies, device physics and control and characterization techniques to algorithm development and quantum operating system design. Our testbed targets a multilevel quantum system (qudit) to expand the accessible Hilbert space of a simple-to-manufacture quantum device and focuses on quantum simulation, typically implemented through custom gates designed with quantum optimal control methods, rather than on a universal computing framework with a fixed gate set. We leverage the Lab’s high-performance computing (HPC) program and related expertise to simulate quantum systems, develop hybrid algorithms, and generate gates optimized for given simulations. Additionally, we have adopted a co-design philosophy from the HPC community in designing new hardware, so that the systems we develop are optimized for the specific physics simulations we plan to use them for.
Thomson scattering (TS) diagnostic is an important diagnostic for measuring electron temperature and density during plasma discharge. However, the measurement of Thomson scattering signal is disturbed by the stray light easily. The stray light sources in the Experimental Advanced Superconducting Tokamak (EAST) TS diagnostic system were analyzed by a simulation model of the diagnostic system, and simulation results show that the dump system is the primary stray light source. Based on the optics theory and the simulation analysis, a novel dump system including an improved beam trap was proposed and installed. The measurement results indicate that the new dump system can reduce more than 60% of the stray light for the diagnostic system, and the influence of stray light on the error of measured density decreases.
Liquid salt-cooled reactors, such as the Fluoride Salt-Cooled High-Temperature Reactor (FHR), offer passive decay heat removal through natural circulation using Direct Reactor Auxiliary Cooling System (DRACS) loops. The behavior of such systems should be well-understood through performance analysis. The advanced system thermal-hydraulics tool System Analysis Module (SAM) from Argonne National Laboratory has been selected for this purpose. The work presented here is part of a larger study in which SAM modeling capabilities are being enhanced for the system analyses of FHR or Molten Salt Reactors (MSR). Liquid salt thermophysical properties have been implemented in SAM, as well as properties of Dowtherm A, which is used as a simulant fluid for scaled experiments, for future code validation studies. Additional physics modules to represent phenomena specific to salt-cooled reactors, such as freezing of coolant, are being implemented in SAM. This study presents a useful first benchmark for the applicability of SAM to liquid salt-cooled reactors: it provides steady-state and transient comparisons for a salt reactor system. A RELAP5-3D model of the Mark-1 Pebble-Bed FHR (Mk1 PB-FHR), and in particular its DRACS loop for emergency heat removal, provides steady state and transient results for flow rates and temperatures in the system that are used here for code-to-code comparison with SAM. The transient studied is a loss of forced circulation with SCRAM event. To the knowledge of the authors, this is the first application of SAM to FHR or any other molten salt reactors. While building these models in SAM, any gaps in the code’s capability to simulate such systems are identified and addressed immediately, or listed as future improvements to the code.
To help bridge the gap between traditional power system engineering instruction and emerging industry needs, a new set of classroom and research tools are needed. Real-time simulation tools emulating power system control room software present an opportunity to introduce students to the array of operational considerations, technical challenges, and decision-making associated with power system operations. The GridAPPS-D platform is proposed to support coursework and academic research as it provides an open-source platform for simulation, application development, and software integration. The GridAPPS-D platform, simulation capabilities, development environment, and interactive training are discussed in the context of lessons-learned from implementation for undergraduate and graduate students in the US and India. A series of potential GridAPPS-D supported academic capabilities are introduced.
Systems of fixed-position radiation sensors can provide information that assists emergency responders following nuclear and radiological incidents. State, local, tribal, and territorial (SLTT) government agencies that implement systems of fixed-position sensors are faced with numerous decisions regarding sensor selection, quantity, and placement. To develop guidance on implementation of radiation detection systems, we simulated the release of radioactive material in an urban environment using a combination of three models: the Weather Research Forecasting (WRF), Quick Urban and Industrial Complex (QUIC), and Monte Carlo N-Particle (MCNP) models. We then evaluated the performance of several hypothetical sensor systems. The small number of simulations we conducted are not sufficient to generate definitive design guidance for radiation sensor systems, but we did identify trends that would be of interest to emergency planners. For a scenario that releases 1000 curies of Cs-137, radiation detectors were needed at 500-meter intervals to have a high likelihood of event detection and to estimate source location and plume detection. We also noted that optimal detector altitude varied with distance to the source. We recommend additional research in this area be conducted to support developing sensor placement guidelines that expand on a range of locations, isotopes, activity levels, and different weather conditions. Original simulation strategies included a range of environments, additional radioisotopes (Am241 and AmBe), and a larger selection of sensor types. These types of expansions would support SLTT guidance on sensor system recommendations.
Hyperdynamics (HD) is a method for accelerating the timescale of standard molecular dynamics (MD). It can be used for simulations of systems with an energy potential landscape that is a collection of basins, separated by barriers, where transitions between basins are infrequent. HD enables the system to escape from a basin more quickly while enabling a statistically accurate renormalization of the simulation time, thus effectively boosting the timescale of the simulation. In [Kim, Perez, Voter, J Chem Phys, 139:144110, 2013)1, a local version of HD was formulated, which exploits the intrinsic locality characteristic typical of most systems to mitigate the poor scaling properties of standard HD as the system size is increased. In this paper, we discuss how both HD and local HD can be formulated to run efficiently in parallel. We have implemented these ideas in the LAMMPS MD code, which means HD can be used with any interatomic potential LAMMPS supports. Together, these parallel methods allow simulations of any size to achieve the time acceleration offered by HD (which can be orders of magnitude), at a cost 3-5x that of standard MD. As examples, we performed two simulations of a million-atom system to model the diffusion and clustering of Pt adatoms on a large patch of Pt(100) surface for 80 and 160 μs.
Abstract Shocked particle-laden flows are important to many natural and industrial processes. When simulating these systems, artificial viscosity is often required to prevent numerical artifacts, such as ringing, from arising in the pressure and density fields. The linear and quadratic coefficients of the artificial viscosity determine the amount of smoothing that occurs in these fields. For particle-laden flows, however, many of the fluid–particle interaction forces, for example, the pressure gradient force and unsteady forces, depend on gradients in the fluid fields. Furthermore, while the shock passes over a particle, these forces can be more dominant than drag. This means that the artificial viscosity coefficients affect how a particle and fluid interact when simulating shocked particle systems. Here this effect is investigated for isolated particles and for a particle curtain using a staggered grid Lagrangian approach. The artificial viscosity coefficients have a significant impact on the maximum force that a fluid imparts to a particle, which is important for determining whether a particle will break up in response to the shock. Furthermore, it is found that the density ratio between the particle and the fluid is important in determining whether the artificial viscosity coefficients have a significant impact on the particle’s motion.
Abstract Quantum computers hold the potential to unlock new discoveries in complex quantum systems by enabling the simulation of physical systems that have heretofore been impossible to implement on classical computers due to intractability. A system of particular interest is that of dirty bosons, whose physics highlights the intriguing interplay of disorder and interactions in quantum systems, playing a central role in describing, for instance, ultracold gases in a random potential, doped quantum magnets, and amorphous superconductors. Here, we demonstrate how quantum computers can be used to elucidate the physics of dirty bosons in one and two dimensions. Specifically, we explore the disorder-induced delocalized-to-localized transition using adiabatic state preparation. In one dimension, the quantum circuits can be compressed to small enough depths for execution on currently available quantum computers. In two dimensions, the compression scheme is no longer applicable, thereby requiring the use of large-scale classical state vector simulations to emulate quantum computer performance. In addition, simulating interacting bosons via emulation of a noisy quantum computer allowed us to study the effect of quantum hardware noise on the physical properties of the simulated system. Our results suggest that scaling laws control how noise modifies observables versus its strength, the circuit depth, and the number of qubits. Moreover, we observe that noise impacts the delocalized and localized phases differently. A better understanding of how noise alters the observed properties of the simulated system is essential for leveraging near-term quantum devices for simulation of dirty bosons, and indeed for condensed matter systems in general.
The purpose of the project is to address the optimization and startup operation of a modular coal direct chemical looping (CDCL) combustion system integrated with a steam cycle for power generation to reduce the risks involved in further scale-up of the technology. The modular reactor design of the CDCL process provides flexibility in the fabrication of the reactor and in its operating capacity (i.e. turndown ratio) at the cost of a more complex heat exchange network (HEN) design and integration. To address the technology gaps and advance the efficiency and economic feasibility of the CDCL technology, the project will perform a detailed and comprehensive analysis of the integration of a modular CDCL reactor system and a steam cycle system under both static and transient conditions via HEN process performance simulations and system dynamic modeling, respectively. The scope of work consists of 1) Experimental and computational studies of the CDCL combustor reactor 2) Comprehensive static (i.e. steady-state) system HEN design analysis in CDCL 550 MWe commercial unit for power generation and 3) Dynamic modeling of site specific design of 10MWe CDCL large pilot plant. The project team has successfully developed and validated a kinetic model for the oxidation of oxygen carriers in the combustor using the unreacted shrinking core model (UCSM). The model is capable of capturing the oxidation kinetics of fully or partially reduced oxygen carrier particles. A computational fluid dynamics (CFD) model is developed to simulate the hydrodynamics, heat transfer, and chemical reaction occurring in the CDCL combustor. The model is developed in MFIX and ANSYS Fluent. Key aspects of CDCL combustor operation, including heat transfer, oxygen carrier oxidation, and the transport of oxygen carrier particles, are simulated using this CFD model. The HEN for a commercial scale 550 MWe CDCL power plant is simulated and optimized using ASPEN Plus. Practical design considerations are incorporated based on industrial experiences. The performance and cost for the commercial CDCL plant is updated based on these analyses. A dynamic model for the 10 MWe CDCL pilot plant is developed in ProTRAX simulation software. The model is based on the pilot plant design developed in project DE-FE0027654 “10 MWe CDCL Large Pilot Plang – Pre-FEED Study” and the steam cycle data obtained from Dover Light & Power plant. The transient behaviors during pilot plant load variation are simulated using the dynamic model.
Power grid modernization and wide-spread distributed energy resource (DER) integration are driving an explosion of interest in advanced algorithms and new optimization schemes. However, there has historically been a lack of open-access realistically sized and detailed datasets-particularly for the distribution system-and simulation tools able to model these systems. This talk will present recent advances with very large-scale (>>1M nodes) distribution data sets and the use of co-simulation to tractably model large integrated transmission-distribution grids and their interactions with new controls/optimization.
Variable air volume (VAV) and variable refrigerant flow (VRF) systems are widely used in office buildings. This study investigated VAV and VRF systems in five typical office buildings in China, and compared their cooling energy use. Site survey and field measurements were conducted to collect the data of building characteristics and operation. Measured cooling electricity use was collected from sub-metering in the five buildings. The sub-metering data normalized by climate and operating hours indicated that the cooling energy consumed by VRF systems was up to 70% lower than that consumed by VAV systems. This was mainly because of the different operation modes of both system types that led to significantly fewer operating hours for the VRF systems. Building simulations were used to quantify the impact of operation modes of VRF and VAV systems on cooling loads. A prototype office building in China was used as the model. The simulation results showed that the VRF operation mode required much lower cooling load when compared to the VAV operation mode. For example, the cooling loads decreased by 42% in Hong Kong and 53% in Qingdao. Key findings include the following: the VRF systems operated in the part-time-part-space mode enabling occupants to turn on the air-conditioning only when needed and when the spaces were occupied. However, the VAV systems operated in the full-time-full-space mode limiting occupants’ control of operation. These findings provide insights into VRF systems operation and controls as well as their energy performance, which could help guide HVAC designers on system selection and building operators or facility managers on system operations to achieve low- or zero-net energy buildings.
The quasi-steady-state approximation (or stochastic averaging principle) is a useful tool in the study of multiscale stochastic systems, giving a practical method by which to reduce the number of degrees of freedom in a model. The method is extended here to slow–fast systems in which the fast variables exhibit metastable behaviour. The key parameter that determines the form of the reduced model is the ratio of the timescale for the switching of the fast variables between metastable states to the timescale for the evolution of the slow variables. The method is illustrated with two examples: one from biochemistry (a fast-species-mediated chemical switch coupled to a slower varying species), and one from ecology (a predator–prey system). Numerical simulations of each model reduction are compared with those of the full system.
Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.
High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.
The study discusses a learning approach for analyzing cyber-events in reactor systems using integrated hardware and personal computer simulator models. Key points include the rise in cyber-attacks and their sophistication in industrial control systems (ICS), the necessity for awareness, understanding, resource allocation, and preparation to combat these threats, and the digital transformation of old and new nuclear plants, increasing their exposure to cyber threats. It highlights the cyber vulnerabilities of advanced reactor systems, which rely on digital instrumentation and control for operations and safety functions, making them susceptible to cyber-attacks. The approach involves demonstrating reactor system plant ICS cyber-attacks under various operational conditions utilizing tools like simulator models and hardware-based kits. A strategic solution approach tailored to critical infrastructure is emphasized, along with community engagement for public and government support, adopting effective learning approaches, and the preparation for anticipated future challenges. The presentation concludes with a call to action to address challenges, leverage opportunities, and advance through lesson learning in cybersecurity for nuclear energy systems.
Proposed thorium-based nuclear fuel cycles are likely to require quantification and verification of 233 U within nuclear material. Because of their similar fission cross sections, active neutron nondestructive assay (NDA) systems may respond similarly to 233 U and 235 U. Traditional safeguards equipment has been optimized for 235 U and 238 U quantification associated with conventional uranium/plutonium fuel cycles and may not be directly applicable to 233 U quantification when mixed with other actinides. This work used models of the large volume active well coincidence counter (LV-AWCC) at Oak Ridge National Laboratory to evaluate the performance of this neutron NDA system to differentiate fissile uranium isotopes. The models were developed to simulate NDA system performance in response to a number of triangular radiation signature training device sources within the central cavity or well. This work predicted that the LV-AWCC can effectively differentiate 233 U from 235 U in certain modes of operation. In active mode, the LV-AWCC with the cadmium liner results in different doubles count rates between the fissile isotopes for a given fissile uranium mass. Without the cadmium liner, the uranium isotopes provide a statistically indistinguishable doubles count rate response for the fissile masses considered in this work (up to approximately 150 g). The cadmium liner serves to harden the neutron interrogation spectrum, which better exploits the notable difference in the 233 U and 235 U fission cross sections at approximately 1 eV. In passive mode, the two fissile isotopes exhibit different doubles and singles count rates regardless of liner presence because the passive source strength of 233 U is approximately 2 orders of magnitude stronger than that of 235 U due to the shorter half-life and correspondingly higher (α, n) yield. We conclude that using neutron interrogation in the LV-AWCC, two measurements are needed to quantify 233 U content in mixed uranium items. The first measurement is used to determine the total fissile uranium mass using a mode that cannot distinguish fissile isotopes (i.e., where a similar response is observed for both fissile uranium isotopes such as active doubles without cadmium or using a thermal neutron interrogation source). In conclusion, the second measurement is used to determine the 233 U content by using a differentiating technique (e.g., passive doubles, passive doubles to singles ratio, active doubles with cadmium).
The Single Primary Heat Extraction and Removal Emulator (SPHERE) and Microreactor Applications Research Validation and Evaluation (MARVEL) were utilized to demonstrate thermal hydraulics codes of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. A recent long-duration test of a sodium heat pipe conducted at the SPHERE facility was utilized for the validation of the heat pipe code Sockeye. The first 10 hours of operation featured a startup of the heat pipe from room temperature; this portion was modeled with Sockeye and compared to the experimental data. Measurements included several thermocouple measurements across the length of the heat pipe surface. Two of Sockeye’s heat pipe models were applied to experiment, as well as several models of the cooling at the condenser end of the heat pipe. Sockeye results gave a decent match to the experimental data, with notable discrepancy in the condenser section, where experimental data indicated a significant inactive length of the heat pipe. The MARVEL microreactor was modeled through a coupled simulation of System Analysis Module (SAM) and SubChannel Module (SCM). This multiscale coupled simulation was performed as an exercise to demonstrate the compatibility and applicability of these codes for thermal-hydraulic analysis of microreactors. The coupling between the SCM and SAM codes for multiscale modeling of MARVEL, was achieved through a domain overlapping approach. In this example a transient SAM simulation provided boundary conditions to SCM, and SCM provided pressure drop to SAM.