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This is a presentation. The outline of the presentation is : Overview multiple grid resiliency projects at Siemens Corporate Technology Introduce AURORA project(AUtonomous and Resilient Operation of energy systems with RenewaAbles) Dive into physics and cyber situation awareness Discuss challenges encountered at building a more resilient distribution grid
The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab's Rapid-Cycling Synchrotron (RCS) operating at 15~Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster's gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.
This paper explores the various configurations of off-line stations and vehicle berthing that have been found to be fundamentally important parameters in the analysis of system performance, operations, and capacity. The proposed Automated Transit Network system concept, which provides a direct ride between a passenger's origin and destination station, is applicable to both fixed guideway automated transit and to systems with self-driving vehicles. Addressed at a conceptual level are the vehicle/station interface in terms of Americans with Disability Act compliance and station capacity concerns. The concept of open-edge platforms monitored by advanced sensing technology combined with artificial intelligence-powered perception for the purpose of protecting passengers from entering the active vehicle lanes is also discussed. Finally, the paper draws initial conclusions on how these station configurations, operational complexities, and associated costs can be better understood through appropriate research and development.
Flexibility in inverter-based loads could be used to support the converter-dominated power grid by offering a rapid, autonomous, and adjustable power reserve during system transients to help maintain system stability. Based on technical potential, ancillary service (AS) value, and implementation costs, this study illustrates the cost-benefit analysis of grid-supportive loads (GSLs) for the supply of fast frequency response (FFR). The net benefit for each GSL is demonstrated using a case study and relevant data sources. The findings suggest that implementation costs for enabling GSL features are low compared to the value that grid operators get from the acquisition of responsive reserve services. The authors believes that, given the rising popularity of renewable energy sources, GSLs can be a useful tool for grid stability in low-inertia systems.
A proposal for an autonomous and flexible ship container inspection system is presented. This could be accomplished by the incorporation of inspection system on the container transportation devices (straddle carriers, yard gentry cranes automated guided vehicles, trailers). This configuration is terminal specific and it will be decided by container terminal operator. In such a way no part of port operational area will be used for inspection. The inspection scenario will include container transfer from ship to transportation device with inspection unit mounted on it, inspection during container movement to the container location. A neutron generator without associated alpha particle detection will be used. This will allow the use of higher neutron intensity (5x10{sup 9} - 10{sup 10} n/s in 4π). The inspected container will be stationary in the 'inspection position' on the transportation device while the 'inspection unit' will move along its side. Following analytical methods will be used simultaneously: neutron radiography, X-ray radiography, neutron activation analysis, (n,γ) and (n,n'γ) reactions, neutron absorption, and scattering, X-ray backscattering, Neutron techniques will take the advantage of using 'smart collimators' for neutrons and gammas, both emitted and detected. The inspected voxel will be defined by intersections/union of neutron generator and detectors solid angles. The container inspection protocol will be based on identification of discrepancies between its cargo manifest and its elemental 'fingerprint' and radiography profiles. In addition, the information on container weight will be obtained during the container transport and foreseen screening from the measurement of density of material in the container. (authors)
Fish passing through dams or other hydraulic structures may be injured or killed despite advances in turbine design, project operations, and other fish bypass systems. The Sensor Fish (SF) device is an autonomous sensor package that characterizes the physical conditions and stressors to which fish are exposed during passage through hydro facilities. It was designed to move passively as a neutrally buoyant object through severe hydraulic environments, while collecting high-resolution sensor data. Since its first generation1, the SF device has been successfully deployed in many fish passage studies and has evolved to be a major tool for characterizing fish passage conditions during fish passage in the Columbia River Basin. To better accelerate hydropower development, the U.S. Department of Energy Water Power Program provided funding to develop a new generation (Gen 2 SF) to incorporate more capabilities and accommodate a wider range of users over a broader range of turbine designs and operating environments. The Gen 2 SF (Figure 1) is approximately the size and density of a yearling salmon smolt and is nearly neutrally buoyant. It contains three-dimensional (3D) rotation sensors, 3D linear acceleration sensors, a pressure sensor, a temperature sensor, a 3D orientation sensor, a radiofrequency (RF) transmitter, and a recovery module2. A low-power microcontroller collects data from the sensors and stores up to 5 min of data on internal flash memory at a sampling frequency of 2048 Hz. The recovery module makes the SF positively buoyant after a pre-programmed period of time, causing it to float to the surface for recovery.
Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors. This evaluation is performed using ground truth data, obtained by measuring the lane positions and transforming them into pixel coordinates. The performance of each method is assessed using the statistical R2 score, indicating the correlation between the detected lane lines and the ground truth. The results show that the U-Net camera-based method exhibits limitations in accurately detecting and aligning the lane lines, particularly in challenging scenarios. However, the radar-based lane detection method demonstrates a strong correlation with the ground truth which implies that the use of this sensor may improve current reliability issues from conventional camera lane detection approach. Furthermore, the study highlights the limitations of the U-Net model for camera lane detection, especially in scenarios with sun glare. This study shows that infrastructure-based radar retro-reflectors can improve autonomous vehicle lane detection reliability. The integration of different sensor modalities and the development of advanced computer vision algorithms are crucial for improving the accuracy, reliability, and energy efficiency of lane detection systems. Addressing these challenges contributes to the advancement of autonomous vehicles and the realization of safer and more efficient transportation systems.
Fluid flow through fractured rock systems is governed in large part by the distribution, interconnectivity, and size of fracture apertures. In-situ stress is one of the primary factors controlling fracture aperture, and one that is altered significantly during high-pressure fluid injections or extractions. Interactions between stress, pore pressure, aperture, and fluid flow can result in complex and evolving poroelastic behavior with significant implications regarding the predictability and risk of developing and managing deep subsurface reservoirs (geothermal, fossil energy, and geologic carbon sequestration). In saturated crystalline rocks, bulk electrical conductivity is highly sensitive to porosity, and therefore to fracture aperture size and distribution. We demonstrate the use of time-lapse 3D electrical resistivity tomography for remotely monitoring stress induced changes in aperture distribution during high pressure injections into a fractured rock system at a scale of tens of meters. Results reveal a complex and continuously evolving stress field involving aperture dilations in the natural fracture system and aperture contractions in adjacent zones of shadow stress. Results provide information about the spatiotemporal changes in the system behavior and point to the potential of electrical imaging for autonomously and remotely monitoring evolving stress conditions by proxy through changes in bulk electrical conductivity.
In monographs of N. V. Azbelev, A. M. Samoilenko, and A. A. Boichuk, constructive methods of study of Noether boundary-value problems have been developed. These methods continue the investigation of periodic problems stated by H. Poincaré, A. M. Lyapunov, N. M. Krylov, N. N. Bogolyubov, I. G. Malkin, and O. Veivoda by the methods of small parameter. We propose an improved scheme of study of autonomous Noether boundary-value problems for nonlinear systems in critical cases. In the case of multiple roots of the equation for generating constants, we obtain sufficient conditions of existence of solutions to an autonomous boundary-value problem not solved with respect to the derivative. The effectiveness of the scheme proposed is illustrated by an example of the periodic problem for the Liénard equation.
This paper presents a novel supervised machine learning (SML) approach for accurate position detection of the receiver coil in wireless power transfer (WPT) systems using only secondary-side electrical measurements, with applications in autonomous unmanned aerial vehicle (UAV) charging. The proposed method trains a supervised learning model to map measured secondary-side voltage and current features to the receiver’s spatial position with high precision. This enables an autonomous UAV to determine its location relative to the primary coil center, the optimal position for maximizing wireless charging efficiency. The sensing method is fully integrated into a standard WPT system, utilizing the same primary and secondary coils for both power transfer and position detection, thereby eliminating additional sensing hardware. The use of a 6.78 MHz operating frequency enhances positional sensitivity, as high-frequency near-field electromagnetic fields respond strongly to small spatial variations. Experimental validation is performed on a 30 W scaled prototype featuring a 210 mm × 140 mm primary coil, a 50 mm × 80 mm receiver coil, and a 15 mm air gap. Results demonstrate reliable position estimation and a strong correlation between predicted position and optimal coil alignment. This integrated framework unifying position detection and wireless charging offers a promising foundation for future autonomous electric vertical takeoff and landing (eVTOL) systems, enabling compact, hardware-efficient, and high-accuracy charging solutions.
The military uses to autonomous platforms to complete missions to provide standoff for the warfighters. However autonomous platforms rely on GPS to provide their global position. In many missions spaces the autonomous platforms may encounter GPS denied environments which limits where the platform operates and requires the warfighters to takes its place. GPS denied environments can occur due to tall building, trees, canyon wall blocking the GPS satellite signals or a lack of coverage. An Inertial Navigation System (INS) uses sensors to detect the vehicle movement and direction its traveling to calculate the vehicle. One of biggest challenges with an INS system is the accuracy and accumulation of errors over time of the sensors. If these challenges can be overcome the INS would provide accurate positioning information to the autonomous vehicle in GPS denied environments and allow them to provide the desired standoff for the warfighters.
Abstract Quantum reservoir engineering is a powerful framework for autonomous quantum state preparation and error correction. However, traditional approaches to reservoir engineering are hindered by unavoidable coherent leakage out of the target state, which imposes an inherent trade off between achievable steady-state state fidelity and stabilization rate. In this work we demonstrate a protocol that achieves trade off-free Bell state stabilization in a qutrit-qubit system realized on a circuit-QED platform. We accomplish this by creating a purely dissipative channel for population transfer into the target state, mediated by strong parametric interactions coupling the second-excited state of a superconducting transmon and the engineered bath resonator. Our scheme achieves a state preparation fidelity of 84% with a stabilization time constant of 339 ns, leading to a 54 ns error-time product in a solid-state quantum information platform.
We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.
The development of a control strategy for the supercritical CO{sub 2} (S-CO{sub 2}) Brayton cycle has been extended to the investigation of alternate control strategies for a Sodium-Cooled Fast Reactor (SFR) nuclear power plant incorporating a S-CO{sub 2} Brayton cycle power converter. The SFR assumed is the 400 MWe (1000 MWt) ABR-1000 preconceptual design incorporating metallic fuel. Three alternative idealized schemes for controlling the reactor side of the plant in combination with the existing automatic control strategy for the S-CO{sub 2} Brayton cycle are explored using the ANL Plant Dynamics Code together with the SAS4A/SASSYS-1 Liquid Metal Reactor (LMR) Analysis Code System coupled together using the iterative coupling formulation previously developed and implemented into the Plant Dynamics Code. The first option assumes that the reactor side can be ideally controlled through movement of control rods and changing the speeds of both the primary and intermediate coolant system sodium pumps such that the intermediate sodium flow rate and inlet temperature to the sodium-to-CO{sub 2} heat exchanger (RHX) remain unvarying while the intermediate sodium outlet temperature changes as the load demand from the electric grid changes and the S-CO{sub 2} cycle conditions adjust according to the S-CO{sub 2} cycle control strategy. For this option, the reactor plant follows an assumed change in load demand from 100 to 0 % nominal at 5 % reduction per minute in a suitable fashion. The second option allows the reactor core power and primary and intermediate coolant system sodium pump flow rates to change autonomously in response to the strong reactivity feedbacks of the metallic fueled core and assumed constant pump torques representing unchanging output from the pump electric motors. The plant behavior to the assumed load demand reduction is surprising close to that calculated for the first option. The only negative result observed is a slight increase in the intermediate inlet sodium temperatures by about 10 C. This temperature rise could presumably be precluded or significantly reduced through fine adjustment of the control rods and pump motors. The third option assumes that the reactor core power and primary and intermediate system flow rates are ideally reduced linearly in a programmed fashion that instantaneously matches the prescribed load demand. The calculated behavior of this idealized case reveals a number of difficulties because the control strategy for the S-CO{sub 2} cycle overcools the reactor potentially resulting in the calculation of sodium bulk freezing and the onset of sodium boiling. The results show that autonomous SFR operation may be viable for the particular assumed load change transient and deserves further investigation for other transients and postulated accidents.
Continuous monitoring and archiving of background radiation levels in and around the nuclear installation is essential and the data would be of immense use during analysis of any untoward incidents. A portable Geiger Muller detector based autonomous gamma dose logger (AGDL) for environmental monitoring is indigenously designed and developed. The system operations are controlled by microcontroller (AT89S52) and the main features of the system are software data acquisition, real time LCD display of radiation level, data archiving at removable compact flash card. The complete system operates on 12 V battery backed up by solar panel and hence the system is totally portable and ideal for field use. The system has been calibrated with Co-60 source (8.1 MBq) at various source-detector distances. The system is field tested and performance evaluation is carried out. This paper covers the design considerations of the hardware, software architecture of the system along with details of the front-end operation of the autonomous gamma dose logger and the data file formats. The data gathered during field testing and inter comparison with GammaTRACER are also presented in the paper. AGDL has shown excellent correlation with energy fluence monitor tuned to identify {sup 41}Ar, proving its utility for real-time plume tracking and source term estimation.
The goal of this project is to develop technologies for further improving our understanding of marine animal interactions with marine hydrokinetic (MHK) devices and for reducing the cost and time to obtain scientifically rigorous behavioral information of fish around hydro turbines.
This project at the Pacific Northwest National Laboratory (PNNL) enhances our understanding of how aquatic life interacts with marine renewable energy (MRE) and hydropower systems through the development and testing of advanced sensor technology. The Marine Sensor Fish (MSF) and its smaller variants, the Sensor Fish Mini (SF Mini) and Flexible Sensor Fish (FSF), measure critical environmental stressors, such as shear forces, pressure changes, and strike impacts, that are essential for assessing the risks posed by energy devices, including tidal turbines, hydropower installations, and other MRE infrastructure. Rigorous field testing has shown that these tools are effective in capturing detailed data on turbulence, pressure variations, strike impacts, and other stressors near these devices, providing valuable insights into the conditions faced by aquatic species. Economically, these tools offer renewable energy developers a practical solution for streamlining environmental assessments and ensuring regulatory compliance, ultimately reducing costs associated with evaluating and mitigating ecological impacts. This research also benefits the public by promoting the growth of renewable energy sources that protect aquatic ecosystems, advancing both sustainable energy production and environmental stewardship.
Shallow subsurface microbial and geochemical processes in watersheds are dynamic and responsive to seasonal and long-term environmental change. At high elevation or latitude, such changes may occur during winter months when normal sampling is impossible, impractical, unsafe, or may actually alter the processes being studied. Yet accurate modeling of biogeochemical parameters in these environments requires sampling that can capture events and persistent cold climate trends. Gaps in current models exist where data have not been collected during extended snow- or ice-covered periods. The goals of our exploratory study were to examine the biogeochemical processes occurring in a well-studied high altitude watershed during winter when the system is largely inaccessible. We tested the hypothesis that during snow cover in the East River (ER) watershed, episodic excursions of microbial community structure and biogeochemical processes and concentrations fluctuated from values extrapolated from pre- and post-snow time periods. Through our research we identified previously unresolved subsurface-surface transport phenomena and a heretofore under-appreciated methane cycle occurring during wintertime months, thus providing valuable insight into watershed processes. Our research showed that autonomous sampling techniques, first used in marine systems, is extremely versatile and can be adapted for use in harsh continental settings to examine temporal changes in freshwater chemistry and microbial community assemblage. Further, we found that by sampling during periods when systems are hard to access, will lend insight and better accountability for global budgets for methane, an important greenhouse gas. By strengthening our knowledge of wintertime biogeochemical relationships in high altitude watersheds our research leads to more complete descriptions of cryptic processes and ultimately will inform and improve the reactive transport models that simulate processes in these systems.