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The present invention relates to systems and methods that use separation of time scales to mitigate interaction between unintentional islanding detection schemes and GSFs.
Developing collaborative research platforms for quantum bit control is crucial for driving innovation in the field, as they enable the exchange of ideas, data, and implementation to achieve more impactful outcomes. Furthermore, considering the high costs associated with quantum experimental setups, collaborative environments are vital for maximizing resource utilization efficiently. However, the lack of dedicated data management platforms presents a significant obstacle to progress, highlighting the necessity for essential assistive tools tailored for this purpose. Current qubit control systems are unable to handle complicated management of extensive calibration data and do not support effectively visualizing intricate quantum experiment outcomes. In this paper, we introduce Qubit Control Storage and Visualization ( QubiCSV ), a platform specifically designed to meet the demands of quantum computing research, focusing on the storage and analysis of calibration and characterization data in qubit control systems. As an open-source tool, QubiCSV facilitates efficient data management of quantum computing, providing data versioning capabilities for data storage and allowing researchers and programmers to interact with qubits in real time. The insightful visualization are developed to interpret complex quantum experiments and optimize qubit performance. QubiCSV not only streamlines the handling of qubit control system data but also improves the user experience with intuitive visualization features, making it a valuable asset for researchers in the quantum computing domain.
Systems, methods and unmanned agents for collaboratively controlling agents in a collaborative network by one or more agents continuously simulating numeric models of one or more other agents in the network to dramatically reduce the computational bandwidth required between agents, and improve the quality of shared estimates of the agent locations as well as the locations and characteristics of other objects of interest, e.g. targets. Bandwidth is reduced by using the models to intelligently filter data before communicating.
Intelligent autonomous sensor networks, often comprised of large numbers of sensors, must be capable of jointly exploiting data collected at each agent in the network, and using that data to optimize their future actions towards multiple mission objectives. Centralized signal processing and optimization solutions process all data and determine all future actions at a single agent, and the resulting information and commands are disseminated back to the network. The communications bandwidth this requires and the single point of failure the central agent represents often make these solutions untenable for national security applications. In this project, several fundamental algorithms for solving both the decentralized signal processing and network optimization were developed, as well as simulation software to validate the results of these algorithms at scale. Specifically, novel algorithms for Bayesian decentralized estimation and decentralized detection and optimization based on the alternating direction method of multipliers (ADMM) were developed for autonomous sensor networks and published in the literature. The first large scale simulation of autonomous sensor networks (1000 agents) was conducted on this project, validating the performance of the developed algorithms. These algorithms and simulation tools are critical components of any decentralized autonomous network and have current and future national security applications, including distributed sensor networks for detection, estimation, and tracking problems, and large decentralized cyber-physical infrastructure such as the power grid.
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Collaboration of networked microgrids (NMGs) with diverse generation sources is a promising solution to smooth volatile generation output and enhance the utilization efficiency of renewable energies. In addition to centralized and decentralized collaboration mechanisms, transactive energy control (TEC) is an emerging and effective market-based control to enable energy transactions among distributed entities such as NMGs. However, existing studies on TEC suffer from several major weaknesses such as unconstrained/simplified model formulations and slow convergence rates. This paper proposes here a novel TEC mechanism to tackle these weaknesses. First, the centralized mechanism, de-centralized mechanism, and subgradient-based TEC mechanism to coordinate the operation of NMGs are briefly reviewed and modeled by a scenario-based stochastic optimization method. A new TEC mechanism is then proposed, consisting of a TEC framework, mathematical model, pricing rule, and algorithm. The optimality of the proposed TEC mathematical model and pricing rule is demonstrated. The effectiveness of the proposed TEC mechanism is verified in case studies where the NMGs operate in grid-connected, islanded, and congested modes. The advantages of the proposed TEC mechanism are also illustrated through comparisons with the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism.
A control architecture and associated control methods are under development to allow a single user to control a team of multiple heterogeneous UMS as they conduct multi-faceted (i.e. multi-objective) missions in real time is disclosed. The control architecture is hierarchical, modular and layered and enables operator interaction at each layer, ensuring the human operator is in close control of the unmanned team at all times. The architecture and key data structures are introduced. Two approaches to distributed collaborative control of heterogeneous unmanned systems are described, including an extension of homogeneous swarm control and a novel application of distributed model predictive control. Initial results are presented, demonstrating heterogeneous UMS teams conducting collaborative missions.
With the potential of increased penetration of connected and autonomous vehicles (CAVs), intersectional signal control faces new challenges in terms of its operation and implementation. One possibility is to fully make use of the communication capabilities of CAVs so that intersectional signal control can be realized by CAVs alone – this leads to non-signalized intersectional operation for traffic networks in urban areas. In this paper, the state-of-the-art on collaborative fault tolerant control schemes for complex systems will be briefly described. This is then followed by the formulation of operational fault tolerant control that realizes the collaborative fault tolerance functionality at CAVs operational level in response to possible individual vehicle faults, where detailed modelling using vehicle movement dynamics will be described together with the construction of fast fault diagnosis and a collaborative fault tolerant control algorithm. A simple example will be given as well to demonstrate the proposed algorithm together with the discussions on other issues such as randomness of the system, communication errors and full energy consideration. These leads to several future directions of the research for the traffic flow control of non-signalized intersections with 100% penetration of CAVs.
The energy economy is continually evolving, particularly in terms of primary energy sources, their conversions to useful forms such as electricity and heat, and their utilization in different sectors, in response to socio-political factors. Because nuclear energy source is a clean and non-carbon-emitting energy source, it is crucial to consider its role in the evolution of the energy economy. Integrated Energy Systems (IES) are collaboratively controlled systems that can dynamically apportion thermal and/or electrical energy to promote the production of various energy products and have the potential to reduce greenhouse gas (GHG) emissions, improve energy efficiency, improve electrical grid dependability, and enhance energy economics. To realize the benefits of IES with the current reactor fleets, selection and development of a complimentary temperature upgrading technology are necessary for providing process heat and improving economics.
Based on a class of industry processes, a distributed fault diagnosis and a collaborative operational fault tolerant control algorithms are presented for an irreversible interconnected stochastic distribution control system with boundary conditions. Different from the existed collaborative fault tolerant controllers, the controller does not need to change after fault occurs, the system tracking performance can be maintained by adjusting the setpoint of the whole system. Simulations are also included to show the effectiveness of the proposed algorithm and encouraging results have been obtained.
As computational density for high-performance computing and big-data services continues to scale, performance scalability of next generation computing systems is becoming increasingly constrained by limitations in memory access, power dissipation and chip packaging. The processor-memory communication bottleneck, a major challenge in current multicore processors due to limited pin-out and power budget, presents a detrimental scaling barrier to data-intensive computing. A consortium team of small businesses and leading researchers that includes experts from photonics processor-memory architecture, III/V photonic laser design/fabrication, silicon photonics design/fabrication, photonics packaging and assembly, and FPGA-based high-performance memory controller IP development – to collaboratively develop a commercialization path for a Photonic Memory Controller Module (P-MCM).
The principal objective of our project is to address how vegetation influences climate variability and precipitation over Amazonian rainforests, with an emphasis on plant physiological controls on deep convection triggering along a geographical water stress gradient. Using a new tool of sun-induced chlorophyll fluorescence (SIF), our study integrates plant physiological observations and climate modeling and aims to decrease the gap between field ecology and climate modeling. We have installed sensors at Manaus site (K34) and at Rebio Jaru (Ji-Paraná, Rondonia). From field observations, we found a diversity of hydraulic functioning, indicating that within the same forest we will find different responses to the predicted climatic changes. The hydraulic strategies varied through a continuum of hydraulic security and water transport efficiency, which can be related to a long process of taxa evolution. The fact that taller trees are more hydraulically vulnerable indicates that if these forests go through higher drought conditions and temperature increase, they could experience a massive tree mortality that will promote changes on forest structure, changing the ecosystem functions, such as water fluxes and carbon storage. To investigate the role of water stress on the onset of the rainy season, we have developed high-resolution cloud resolving model (CRM) simulations using a new strategy coupling convective and large-scales. The first results show that the presence of early morning fog reduces the wet season transpiration and carbon uptake over the everwet regions of Amazonia. We have incorporated SIF into CLM to constrain energy, carbon and water flux of tropical forests. By using CloudSat-observed cloud vertical structure, we show an empirical relationship between atmospheric energetics and convective clouds in Amazonia, in comparison with the savanna/shrubland and tropical West Pacific, emphasizing predominant deep convection during the early wet season when Bowen ratio and convective available potential energy (CAPE) increase. Although on average the deep convections are prominent only during the short dry-to-wet transition while shallow/congestus clouds dominate the rest of the year in Amazonia, there is a clear cloud regime shift when CAPE grows beyond a threshold value. Our project helped initiate and maintain collaboration between US and Brazilian scientists. We have been collaborating through yearly field campaigns, weekly international research meetings, and ongoing other collaborative projects. Through these activities, we helped our students and postdocs fill their knowledge gaps and learn new research techniques.
We developed and deployed a robust and accurate instrumentation package for measurement of solar induced chlorophyll fluorescence (SIF) from above a plant canopy. The sensor collects light from identical up and down facing hemispheric collectors. A fiber optic transmits the collected light (selected by a rotatable prism) to spectrometers located at the base of the tower. Spectra of the down welling and upwelling light were collected alternatively and stored for later analysis. SIF was retrieved by a spectral unmixing approach using high resolution spectra in the 730-760 nm band. Most other ground-based sensors base their analysis on O 2 -alpha band (760-780 nm) for SIF retrieval. We found that the former gave identical results when the atmosphere was clear, but the former was more stable in the cloudy conditions that often prevailed in the Amazon. The mechanical design and software developed for the system are available on request. The sensor was mounted near the top of an 80 m tower at K34 near Manaus in the Brazilian Amazon Forest. The sensor system proved weather-tight and functioned flawlessly over the time of deployment. Our experiment was cut short at 6 months by failure of air conditioning infrastructure at the tower. We found that SIF emitted by canopy was remarkably stable when scaled by the downwelling photosynthetically active radiation or the upwelling near infrared light. We detected a 3.4% increase in SIF over the course of the dry season. This finding is consistent with the observation that several trees in the field of view flushed new leaves in the dry season. We saw no evidence of drought stress or photo damage of the forest canopy during our study.
Testing of Cryomodule first articles at PIP-II's on-site Cryomodule Test Facility (CMTF), where EPICS based controls are already being used, has been invaluable to the project. In addition to validating hardware and controls components, it has provided opportunities to build buy-in and illicit meaningful feedback early-on. A device naming convention has also been significantly refined with an internal working group and the PIP-II integration team. Compressed air and Process Control Water (PCW) systems have been installed, and operations has been engaged in Phoebus display development in preparation for a transition to operations for these systems by 2026. Cryoplant hardware has been put in place, and collaboration between controls and PIP-IIs cryo group for IOC and display development is well underway for commissioning of cryoplant systems to begin early 2026. The project has also received beneficial occupancy of the High-Bay Building in October 2025, keeping us on schedule for Warm Front End commissioning to begin in 2027.
The IDEATE project recognized that this is a critical point in history where advanced engineering solutions are required to propel the U.S. automotive industry irrevocably to the next level: the electrified drivetrain. The existing workforce is not sufficient to this task. It is imperative that automotive engineers with traditional backgrounds focused on internal-combustion and mechanical-drivetrain technologies be retrained with the most current advanced solutions in battery controls and vehicle power electronics. For the long-term success of the U.S. automakers, it is even more important that a future workforce be developed that has both broad and deep comprehension of the issues involved and the most advanced solutions known to these problems. Two campuses of the University of Colorado system joined forces to establish the GATE Center of Excellence in Innovative Drivetrains in Electric Automotive Technology Education (IDEATE). The University of Colorado Boulder (CU-Boulder) is widely regarded as having one of the top graduate programs in power electronics in the country; the University of Colorado Colorado Springs (UCCS) has unrivaled expertise in algorithms for automotive battery control. By collaborating, IDEATE built on our team’s proven strengths to develop innovative curricula and to initiate courses and programs that have provided (and continue to provide) students with a unique opportunity for holistic and specialty education in electric drivetrain technology (part-way through the project, we invited Utah State University to join the project, adding to our strength in power electronics for battery application).
This collaborative project integrated an improved version of the Artificial Intelligence/Deep Learning FRNN prediction and control software into the real-time DIII-D PCS (plasma control system). A key AI/DL software challenge is to build a modern high-performance computing (HPC) enabled “synthetic plasma simulator” capable of carrying out HPC-driven real-time plasma control applications. This involves development of a deep learning framework to train the surrogate model for a first-principles-based instability analysis simulator (“SGTC”) derived from the global gyrokinetic code GTC. The role of SGTC is to provide accurate and detailed plasma instability information from a real-time AI-based simulator capability to complement the deep learning prediction and control from experimentally-measured signals, such as ECE Imaging, supplemented by synthetic SGTC-ECEI.
With increasing penetrations of volatile renewable generation and cyber-physical disruptions, ensuring the safe operation of bulk power systems has become unprecedentedly challenging. Because communication and computational costs restrict centralized system dispatch to being called upon every few minutes, and because purely local schemes are shown to be insufficient, distributed controls have been advocated for handling unanticipated system conditions in real time. The applicability of distributed control schemes, however, is fundamentally limited by their need for widespread communication and model cognizance. In this context, we put forth a hybrid, low-communication, saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a "hotline" communication network. Under this protocol, when agents observe a constraint violation based on local measurements, they respond locally until their control resources saturate, in which case they send a beacon for assistance to peer agents. The scheme ensures that minor violations are efficiently mitigated via fast local controls, whereas severe violations can be handled by collaboration among a relatively small set of agents. We evaluate the performance of this scheme via numerical tests on the IEEE 14-bus test feeder, where agents act upon noisy measurements under diverse scenarios of load variations and severe low-/high-voltage events.
In this project, the relationship between natural organic matter (NOM) and two radioactive elements that are relevant to nuclear waste disposal were studied: Plutonium (Pu) and Iodine. The human and environmental risks associated with Pu stem mainly from the very long half-lives of several of its isotopes ( 238 Pu, 88 yr; 239 Pu, 24,100 yr; 240 Pu, 6560 yr) and its radiotoxicity. Understanding Pu biogeochemical behavior in both near-field (>10-11M) and far-field scales (<10-11M) is imperative to the development of approaches for reprocessing Pu, remediation of Pu contamination and accurate assessment of risks posed by disposal practices for Pu-bearing wastes. The environmental mobility of Pu can be affected by redox potential, pH, adsorption, precipitation, complexation, colloid formation, and microbial activity, of which the first characteristic has the most profound influence. Numerous studies have shown high affinity of Pu towards NOM, as well as to mineral phases. NOM is ubiquitous in the environment, e.g., both fulvic and humic acids are able to reduce Pu(V,VI) to Pu(IV) and the redox potential of NOM is positively related to the abundance of phenolic/acidic OH groups. NOM can either facilitate or limit actinide migration, depending on specific biogeochemical conditions including pH, mineral and organic matter characteristics, etc. The other radionuclide of interest is radioiodine ( 129 I). 129I is a major by-product of nuclear fission and of serious concern to the Department of Energy (DOE) as it is among the top risk drivers at existing and potential radiowaste-contaminated sites. The risk of 129 I stems largely from its high bioconcentration factor (90% of the body’s iodine is accumulated in the thyroid), a high inventory at source terms, a very long-half life (16M years), and rapid mobility in the subsurface environment. As a consequence, 129 I has the lowest drinking water standard (1 pCi/L) among all radionuclides in the Federal Register. With a novel and sensitive gas chromatography-mass spectrometry (GC-MS) method developed in our lab, it is possible to quickly and simultaneously determine the distribution of 129 I and stable 127 I forms in environments, as low as 2 pCi/L for 129 I. This method was subsequently validated using accelerator mass spectroscopy, AMS. IO 3 - and organo-I were determined as major species in the groundwater of SRS and the Hanford Site, contrary to thermodynamic predictions that I- should be the dominant species at these sites. Mobility of 129 I was also demonstrated to depend greatly on the I species and its concentration, sediment pH, and redox state, with times to achieve equilibrium taking up to 12 weeks. Along the groundwater pathway in the F-Area of SRS, 129 I- supplied from the seepage basins was transformed to 129 IO 3 - and organo- 129 I with increasing iodine sediment sorption, causing the lower total 127 I and 129 I concentrations along the gradient transect of the waste plume. By contrast, groundwater 129 I concentrations in the wetlands (as high as 1617.3 pCi/L) were greatly elevated with respect to the source term (159.3 pCi/L). While the NOM promoted the uptake of 129 I to the wetland sediment, it also promoted the formation of soluble organic fraction. A small fraction of NOM that is bound to iodine can behave as a mobile organo-I source. Iodide was enzymatically incorporated into NOM, whereas both iodide and iodate were abiotically bound to NOM, under certain conditions. Iodate removal from the mobile aqueous phase can also occur through incorporation into carbonate (e.g., at the Hanford Site, USA). Thus immobilization and re-mobilization of iodine species were influenced by pH, Eh and the presence of NOM and metal oxides, which adds to the complexity of site remediation action. A ground-breaking result was to elucidate the products (i.e. organo-iodine moieties formed via enzymatic and non-enzymatic processes) at the molecular level by nuclear magnetic resonance (NMR) and electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICRMS). We found that iodine-NOM interactions may be influenced by NOM hydrophobic aliphatic moieties. From the perspective of ESI-FTICRMS, organo-iodine formulas were ascribed to the groups of unsaturated hydrocarbons, lignins and proteins. Iodate is likely abiotically reduced to reactive iodine species by lignin- and tannin-like compounds or carboxylic-rich alicyclic molecules (CRAM). We also investigated microbial mechanisms in iodine incorporation into NOM. We established that soil bacteria isolated from F-Area of SRS did not accumulate significant amounts of I- (0.2-2%). Intracellular uptake of I- decreases with increasing pH when pH ranged from 4 to 6. In contrast, 44 out of 84 strains isolated from the F-Area of SRS can transform I- to IO 3 - and organo-iodine. In some cases, oxidation was facilitated in the presence of H 2 O 2 . Microbes can also excrete organic acids that enhance I- oxidation by lowering the ambient pH and reacting with H 2 O 2 to form peroxy carboxylic acids. At lower pH values (≤5), H 2 O 2 hydrolysis was the driving force for iodide-oxidation; whereas, at pH ≥ 6, spontaneous decomposition of peroxy carboxylic acids, originating from H 2 O 2 and organic acids were the primary cause of iodide oxidation. Lastly, it was determined that microbial processes involved in Mn (II) are capable of directly oxidizing I- via enzymatic catalysis (i.e., multicopper oxidases), or indirectly through the formation of reactive oxygen species (ROS) and/or biogenic manganese oxides. ROS-mediated oxidation of I- was found to predominate at pH >5, whereas the enzymatic and Mn oxide pathways were more active at pH < 5. Together, this project has resulted in 9 publications in high-impact journals, and the training of 1 Ph.D and 4 undergraduate students.