Mixed-Integer Programming of The Constellation Scheduling Problem.
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We introduce the problem of scheduling observations on a constellation of remote sensors, to maximize the aggregate quality of the collections obtained. While automated tools exist to schedule remote sensors, they are often based on heuristic scheduling techniques, which typically fail to provide bounds on the quality of the resultant schedules. To address this issue, we first introduce a novel deterministic mixed-integer programming (MIP) model for scheduling a constellation of one to n satellites, which relies on extensive pre-computations associated with orbital propagators and sensor collection simulators to mitigate model size and complexity. Our MIP model captures realistic and complex constellation-target geometries, with solutions providing optimality guarantees. We then extend our base deterministic MIP model to obtain two-stage and three-stage stochastic MIP models that proactively schedule to maximize expected collection quality across a set of scenarios representing cloud cover uncertainty. Our experimental conclusions on instances of one and two satellites demonstrate that our stochastic MIP models yield significantly improved collection quality relative to our base deterministic MIP model. We further demonstrate that commercial off-the-shelf MIP solvers can produce provably optimal or near-optimal schedules from these models in time frames suitable for sensor operations.
Constellation Energy and the United States Department of Energy (DOE) have established a public/private partnership to implement a pilot digital upgrade to replace legacy analog, safety-related reactor protection and emergency safety feature actuation systems (RPS/ESFAS) with modern digital systems. This effort is occurring at Constellation’s Limerick Generating Station. This project is being performed in accordance with industry processes that have been adapted to better support digital upgrades. These processes include IP-ENG-001, Standard Design Process, NISP-EN-04, Standard Digital Engineering Process and Electric Power Research Institute Report 3002011816, Digital Engineering Guide. The Light Water Reactor Sustainability (LWRS) Program at the Idaho National Laboratory (INL) has been supporting this effort. Latest INL HFE efforts in support of this project have focused on definition and implementation of the Human Factors Engineering (HFE) Program in support of Constellation design and related licensing efforts. This paper will present the development of the HFE 3D modeling execution of the Main Control Room safety upgrades. New equipment and modifications are planned for upgrading the new safety systems. 3D modeling allows for visualization of the new equipment and modifications to be shared with engineering and design teams for evaluation and review. Anthropometric considerations such as sight lines and functional reach can be evaluated directly using the 3D model. This effort has helped to provide visualizations for additional review with operations personnel and project stakeholders.
Constellation Energy and the United States Department of Energy (DOE) have established a public/private partnership to implement a pilot digital upgrade to replace legacy analog, safety-related reactor protection and emergency safety feature actuation systems (RPS/ESFAS) with modern digital systems. This effort is occurring at Constellation’s Limerick Generating Station. This project is being performed in accordance with industry processes that have been adapted to better support digital upgrades. These processes include IP-ENG-001, Standard Design Process, NISP-EN-04, Standard Digital Engineering Process and Electric Power Research Institute Report 3002011816, Digital Engineering Guide. The Light Water Reactor Sustainability (LWRS) Program at the Idaho National Laboratory (INL) has been supporting this effort. Latest INL HFE efforts in support of this project have focused on definition and implementation of the Human Factors Engineering (HFE) Program in support of Constellation design and related licensing efforts. This paper presents the development execution of the HFE Program through completion of the Planning and Analysis Phase as defined by NUREG-0711 as well as HFE lessons learned.
Abstract We present the final results of our search for new Milky Way (MW) satellites using the data from the Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP) survey over ∼1140 deg2. In addition to three candidates that we have already reported, we have identified two new MW satellite candidates in the constellations of Sextans, at a heliocentric distance of D⊙ ≃ 126 kpc, and Virgo, at D⊙ ≃ 151 kpc, named Sextans II and Virgo III, respectively. Their luminosities (Sext II: MV ≃ −3.9 mag; Vir III: MV ≃ −2.7 mag) and half-light radii (Sext II: rh ≃ 154 pc; Vir III: rh ≃ 44 pc) place them in the region of size–luminosity space of ultra-faint dwarf galaxies (UFDs). Including four previously known satellites, there are a total of nine satellites in the HSC-SSP footprint. This discovery rate of UFDs is much higher than that predicted from the recent models for the expected population of MW satellites in the framework of cold dark matter models, thereby suggesting that we encounter a too many satellites problem. Possible solutions to settle this tension are also discussed.
Hyperspectral imaging is a very promising technology for nuclear proliferation detection. However, due to size and weight restrictions, small hyperspectral platforms such as satellites and small drones lack the on-board computing resources for accurate, real-time analysis of the enormous flow of data that a continuously operating hyperspectral sensor generates. This severely limits satellite systems, which can collect far more data than what they can telemeter, and hinders the ability of all platforms to adapt their missions on the fly in response to observations. This program addresses the hyperspectral data processing challenge through development of new, fast and accurate algorithms that produce data products in real time. The algorithms circumvent the major computational bottlenecks in existing processing streams, and would be incorporated in lightweight, power-efficient single-board computer systems. The toolkit of fast algorithms will be immediately useful in current and future hyperspectral systems being built by the Government and by private industry, including drone-based systems and satellite constellations that acquire timely global imagery.
The Treaty on the Prohibition of Nuclear Weapons calls for states to meet regularly to consider measures for the verified, time-bound and irreversible elimination of nuclear weapon programs. Key to this effort is mapping a comprehensive set of nuclear weapon program indicators and further developing international capacity to monitor them. Distinct from the deadlocked Conference on Disarmament, this new forum will have an opportunity to examine and elaborate disarmament verification concepts. One relevant concept is a Nuclear Disarmament and Nonproliferation International Monitoring System (NDN-IMS), elaborated from a proposal most recently noted by the United Nations Open-ended Working Group (A/71/371). As a flexible framework, an NDN-IMS, could help unite diverse technologies and mechanisms around the purpose of disarmament verification in ways that support the existing nonproliferation regime and the Nuclear Nonproliferation Treaty (NPT). Elements could include satellite imagery from new constellations, wide area environmental sampling, and civil society reporting.
EXECUTIVE SUMMARY The Light Water Reactor Sustainability Program Plant Modernization Pathway has been working with industry for a number of years to leverage digital technology to extend the life and improve the performance of the existing fleet through modernized technologies and improved processes for plant operation and power generation. This includes development of modernization solutions to improve reliability and economic performance while addressing US nuclear industry’s aging and obsolescence challenges. The objective of these efforts is to deliver a sustainable business model that enables US nuclear industry to remain competitive. Digital Infrastructure (DI) research has established a technical foundation for these efforts. This effort began with technical analysis and support for a safety-related instrumentation and control (I&C) pilot upgrade being performed at Constellation Energy Generation’s Limerick Nuclear Plant. The following publicly available reports were produced as part of this effort. • INL/EXT-20-61079, Vendor-Independent Design Requirements for a Boiling Water Reactor Safety System Upgrade [1] • INL/EXT-20-59371, Business Case Analysis for Digital Safety-Related Instrumentation & Control System Modernizations [2] • INL/EXT-20-59809, “Safety-Related Instrumentation and Control Pilot Upgrade: Initial Scoping Phase Implementation Report and Lessons Learned [3] • INL/RPT-23-72105, Safety-Related Instrumentation and Control Upgrade: Conceptual – Detailed Design Phase Report and Lessons Learned [4]
The integrated Miniaturized Electrostatic Analyzer-Reflight (iMESA-R) is a space weather instrument designed to measure plasma density, temperature, and spacecraft charging, along with total ionizing dose and dose rate. A constellation consisting of five nearly identical instruments has been designed and developed to act as science payloads hosted on-board Department of Defense satellites operating in low-Earth orbit. To validate the dosimetry component of the iMESA-R, a radiation test study was performed to calibrate the dosimeter and ascertain the attenuation due to the instrument aluminum housing. An 80 curie Cobalt-60 radioisotope source emitting beta, X-ray, and gamma ray radiation decay products was used to calibrate the dosimeter response as a function of distance and instrument shielding. We present results of the calibration study along with initial on-orbit data presented from the first operational iMESA-R hosted on-board Space Test Program Satellite 5 in a polar Earth orbit. The initial on-orbit data demonstrate the ability to map the radiation environment, particularly the South Atlantic Anomaly and the auroral regions using total ionizing dose rate due to electrons of E > 1.5 MeV and protons of E > 25 MeV.
Despite their centrality to life on Earth, we know little about how microbes interact with each other, their hosts, or their environment. Although DNA sequencing technologies have enabled a new view of the ubiquity and diversity of microorganisms, this has mainly yielded snapshots that shed limited light on microbial functions or community dynamics. Given that nearly every habitat and organism hosts a diverse constellation of microorganisms—its “microbiome”—such knowledge could transform our understanding of the world and launch innovations in agriculture, energy, health, the environment, and more (see the photo). Here, we propose an interdisciplinary Unified Microbiome Initiative (UMI) to discover and advance tools to understand and harness the capabilities of Earth's microbial ecosystems. The impacts of oceans and soil microbes on atmospheric CO 2 are critical for understanding climate change. By manipulating interactions at the root-soil-microbe interface, we may reduce agricultural pesticide, fertilizer, and water use enrich marginal land and rehabilitate degraded soils. Microbes can degrade plant cell walls (for biofuels), and synthesize myriad small molecules for new bioproducts, including antibiotics. Restoring normal human microbial ecosystems can save lives [e.g., fecal microbiome transplantation for Clostridium difficile infections]. Rational management of microbial communities in and around us has implications for asthma, diabetes, obesity, infectious diseases, psychiatric illnesses, and other afflictions. The human microbiome is a target and a source for new drugs and an essential tool for precision medicine. The National Science Foundation's Microbial Observatories, the U.S. Department of Energy's Genomic Sciences program, the National Institutes of Health's Human Microbiome Project, and other efforts in the United States and abroad have served as critical first steps in revealing the diversity of microbes and their communities. However, we lack many tools required to advance beyond descriptive approaches to studies that enable a mechanistic, predictive, and actionable understanding of global microbiome processes. Developing these tools requires new collaborations between physical, life, and biomedical sciences; engineering; and other disciplines.
Remote sensing systems have firmly established a role in providing immense value to commercial industry, scientific exploration, and national security. Continued maturation of sensing technology has reduced the cost of deploying highly-capable sensors while at the same time increased reliance on the information these sensors can provide. The demand for time on these sensors is unlikely to diminish. Coordination of next-generation sensor systems, larger constellations of satellites, unmanned aerial vehicles, ground telescopes, etc. is prohibitively complex for existing heuristics-based scheduling techniques. The project was a two-year collaboration spanning multiple Sandia centers and included a partnership with Texas A&M University. We have developed algorithms and software for collection scheduling, remote sensor field-of-view pointing models, and bandwidth-constrained prioritization of sensor data. Our approach followed best practices from the operations research and computational geometry communities. These models provide several advantages over state of the art techniques. In particular, our approach is more flexible compared to heuristics that tightly couple models and solution techniques. First, our mixed-integer linear models afford a rigorous analysis so that sensor planners can quantitatively describe a schedule relative to the best possible. Optimal or near-optimal schedules can be produced with commercial solvers in operational run-times. These models can be modified and extended to incorporate different scheduling and resource constraints and objective function definitions. Further, we have extended these models to proactively schedule sensors under weather and ad hoc collection uncertainty. This approach stands in contrast to existing deterministic schedulers which assume a single future weather or ad hoc collection scenario. The field-of-view pointing algorithm produces a mosaic with the fewest number of images required to fully cover a region of interest. The bandwidth-constrained algorithms find the highest priority information that can be transmitted. All of these are based on mixed-integer linear programs so that, in the future, collection scheduling, field-of-view, and bandwidth prioritization can be combined into a single problem. Experiments conducted using the developed models, commercial solvers, and benchmark datasets have demonstrated that proactively scheduling against uncertainty regularly and significantly outperforms deterministic schedulers.
Our team made substantial technical progress on various fronts during the course of the program. Multiple milestones were geared towards demonstrating the feasibility of machine learning based predictive maintenance digital twins towards reducing O&M costs, whereas some other milestones actually focused on identifying technical gaps and developing technologies such as humble AI to provide necessary robustness to the ML-based models. We were able to demonstrate in many cases that Machine learning-based methods can be successfully adapted for Nuclear plant environments especially for remote monitoring applications. Detailed analyses were carried out with plant and full scope simulation data along with capabilities of enhanced analytics to assess and set realistic expectations on cost reductions in O&M. These assessments are paving the way for investments towards reactor design improvements as well project planning for SMR projects as they develop and mature in the next few years. Technology developed under this program got direct visibility to GE Hitachi and their utility customers and resulted in positive intents to deploy some of the elements from design phase. The project additionally resulted in several reports, publications, software and data generation that will be useful in deployment and O&M services for BWRX300 fleets.
The primary operational costs for existing nuclear reactors are plant operation costs, maintenance costs, and fuel costs, all of which are influenced by the materials used and the design of the reactor core. Optimizing core design parameters—including burnup limits and enrichment levels—can lengthen cycles, reduce outages, reduce reload batch fractions and spent fuel storage requirements, and lower maintenance and operating expenses, thereby enhancing economic viability. Furthermore, developing higher-fidelity tools to simulate these parameters enables better identification of the available margin, improves overall plant safety, and improves the understanding a given plant’s responses to accident scenarios. Here, in the US, much of the research and development focus has traditionally been on pressurized water reactors (PWRs), but boiling water reactors (BWRs) comprise approximately one-third of the US reactor fleet. Modeling and simulation advances for BWRs and PWRs—particularly those achieved through the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program—are crucial to the long-term viability of the light–water reactor industry. A key research area of the high burnup and increased enriched fuel initiative is focused on addressing issues related to postulated loss-of-coolant accident (LOCA) scenarios. NEAMS has dedicated significant effort to enhancing tools to better support BWRs. A current focus is showcasing the BWR framework for high-burnup LOCA analysis. This high-fidelity steady-state analysis is a first step toward demonstrating a best-estimate, pin-by-pin high-burnup BWR LOCA analysis to assess full-core cladding rupture behavior for a representative BWR. The objective of this effort is to provide a modeling capability that will help elucidate and provide a best-estimate evaluation for cladding rupture susceptibility in BWRs. This modeling capability could then be used to prevent and/or mitigate cladding ruptures in postulated accident scenarios without penalizing operational parameters. Additionally, the results of this work will help identify strategies for finding additional margins or potentially limiting cladding ruptures through core design optimizations to enable more efficient core designs.
Accurately assessing high-burnup fuel behavior during loss-of-coolant accidents (LOCAs) is essential for understanding fuel fragmentation, relocation, and dispersal (FFRD) risks across the US light-water reactor fleet. This work updates previous Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program multiphysics LOCA analyses for a pressurized water reactor (PWR) and a boiling water reactor (BWR) by incorporating recent model and material property advancements in the BISON fuel performance code, including a high-burnup structure (HBS) model, revised cladding burst criteria, and updated thermal–mechanical correlations. This update was needed to support ongoing industry initiatives and upcoming regulatory changes. Full-core, rod-resolved operating histories generated using Virtual Environment for Reactor Analysis (VERA) and system-level LOCA conditions obtained from TRACE were applied to statistically representative rod samples in BISON to evaluate burst behavior and FFRD susceptibility. These calculations used two cladding burst correlations and three fuel pulverization models so that the predictions of these models could be compared. The updated PWR simulations show markedly improved numerical stability as the number of crashed simulations decreased by 95% compared to the previous study, and hence higher confidence in results. The updated PWR simulations predicted cladding bursts exclusively among once-burned, high-power rods, with two different cladding burst models identifying the same burst-susceptible population. Resulting FFRD susceptibility estimates are significantly reduced compared with earlier studies, driven by cooler predicted fuel and plenum temperatures, lower hoop strains, and reduced fission gas release in the updated models. In contrast, none of the BWR rods were predicted to burst under either burst criterion, reaffirming minimal BWR FFRD susceptibility even with updated HBS and material models. Comparisons between the PWR and BWR end-of-cycle predictions are made. Comparison with prior work highlights significant shifts in PWR fuel performance metrics and confirmation of earlier BWR conclusions. Overall, the updated results underscore the importance of having high-resolution detailed modeling capability and continuously integrating evolving material models and physics into high-resolution multiphysics simulations. The unified assessment presented here strengthens confidence in predicting high-burnup LOCA behavior by improving agreement between different cladding burst correlations. These results also provide an improved foundation for future BISON model development, FFRD susceptibility calculations.
The US nuclear industry is looking to improve on the operating economics of the current fleet of light-water reactors (LWRs). One way of achieving this is by operating fuel to higher burnup. In pressurized water reactors (PWRs), relaxing the current burnup limit will allow for cycle length extensions and power uprates; in boiling water reactors (BWRs) it may allow for improved fuel utilization and reduced feed assemblies, as well as more efficient power uprates and increased capacity factors that will support the Administration’s Executive Order to facilitate 5 GW of power uprates at existing nuclear facilities. However, one of the key limitations to operating fuel to higher burnup is the risk of fuel fragmentation, relocation, and dispersal (FFRD). Recognizing the high interest in extending burnup limits, the US Nuclear Regulatory Commission (NRC) has issued Draft Regulatory Guide DG-1434, which defines an approach that would be acceptable to the NRC for addressing FFRD risk. The approach defined will require better understanding of the phenomena leading to FFRD as well as best-estimate simulation methods to understand FFRD risk in high-burnup cores. The Nuclear Energy Advanced Modeling and Simulation program is supporting the FFRD industry challenge problem through development of state-of-the-art, high-fidelity modeling and simulation LWR analysis capabilities; namely, the BISON fuel performance code and the VERA core simulator software. These tools, along with the US NRC TRACE system analysis code, have been utilized for analysis of FFRD risk in both PWR and BWR cores in recent years. The work documented in this report addresses the lack of high-fidelity research for BWRs and builds on a previous activity where the framework has been applied to Cycles 16 through 18 of Limerick Unit 1, a BWR/4, with introduction of 8 high-burnup lead use assemblies (HBLUAs) that were representative of the 8 HBLUAs loaded into Limerick Unit 2 in 2021. VERA was used in this previous activity to model rod-by-rod depletion in these cycles, and its solution was used to initialize a TRACE simulation of a large-break loss-of-coolant accident (LBLOCA) at the end of Cycle 18. In the work documented in this report, the TRACE model was improved by refining the core mesh and utilizing a new feature that allows for capturing the full 3D VERA power distribution in the model. This allows for a more detailed solution for setting BISON boundary conditions. Furthermore, the solutions from VERA and TRACE were used to set up and perform BISON simulations of about 1,000 rods sampled from the core, including all burnup levels. Utilizing two cladding burst models, it was shown that no fuel rods were predicted to burst during the postulated LBLOCA transient. Additionally, a sensitivity study was performed by artificially increasing linear heat rate during the postulated LBLOCA to identify parameters that correlate with rod burst susceptibility. Burnup, fission gas release, and hoop strain were all found to be positively correlated with rod burst susceptibility. Small-break loss-of-coolant accident (SBLOCA) analyses were also performed; these analyses predicted cladding temperature increases that were bounded by the LBLOCA cladding temperatures for all small break sizes studied for this plant. However, future refinements to the plant response assumptions during the SBLOCA could impact the predicted cladding response. Finally, a benchmark study was performed between CTF and TRACE for LOCA conditions to better qualify CTF for BWR LOCA modeling.