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DOE OSTI2026

Responsible Adoption of Artificial Intelligence (AI) in Electric Grid Operations

The future of the grid will be powered by AI—or undermined by it. Artificial intelligence is rapidly reshaping grid operations, improving fault detection, forecasting accuracy, and real-time optimization. As AI systems move closer to operational decision loops, however, they introduce new consequence pathways: expanded attack surfaces, model integrity risks, regulatory exposure, and human-automation challenges. This talk presents a consequence-driven framework for deploying AI responsibly in the electric grid. Attendees will gain practical strategies to strengthen resilience, boost reliability, and deploy AI securely — ensuring the grid of the future is not only smarter but safer.

25 - ENERGY STORAGE↗

DOE OSTI2024

Approaches for Autonomous Peer to Peer Communication: For Resilient Community Microgrids

The final phase of the Islandable Communications task of the SECURE project consisted of integrating the islandable communications network into a microgrid simulation at NREL. Initially integration was planned for a microgrid implemented as a mix of simulation and physical hardware. As the project evolved, the microgrid became fully simulated. The simulation environment was structured to break out the communication paths that would pass over a communication network in a field demonstration. The Islandable Communication network was integrated into the simulation environment, allowing both a commercial cellular carrier and a Private LTE network to be evaluated. The microgrid simulation showed differences in performance and behavior when operating over the wireless networks (compared to operating over wired Ethernet in the lab). These differences point to further research opportunities to better understand how microgrid communication carried over real-world or impaired networks affects the microgrid system behavior.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Impacts of Voltage-Based Grid Support Functions on Energy Production of PV Customers

This paper presents the impact of inverter grid- support functions (GSFs) on photovoltaic (PV) customer energy production on a real distribution feeder in Oahu, HI. These autonomous GSFs based on local voltage measurements are good alternatives to increase PV hosting capacity. However, these functions can result in PV energy curtailment to the customer, and this study addresses the concerns about the impact of inverter GSFs through detailed quasi-static time series (QSTS) simulations. It proposes four metrics: maximum and average GSF curtailment, average increased generation and average net generation change, to assess the impact of a given control on PV systems located on customer sites. It was found that curtailment of PV production is negligible for customers where peak voltage is within ANSI C84.1 range. A reliable relationship between curtailment and peak customer voltage is demonstrated, suggesting that peak voltage could be used as an indicator of customer curtailment.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Failure Mechanism Traceability and Application in Human System Interface of Nuclear Power Plants using RESHA

In recent years, there has been considerable effort to modernize existing and new nuclear power plants with digital instrumentation and control systems (DI&C). However, there has also been considerable concern both by industry and regulatory bodies for the risk and consequence analysis of these systems. Of particular concern are digital common cause failures (CCFs) specifically related to software defects. These “misbehaviors” by the software can occur in both the control and monitoring of a system. While many new methods have been proposed to identify potential software failure modes, such as Systems-theoretic Process Analysis (STPA), Hazard and Consequence Analysis for Digital Systems (HAZCADS), etc., these methods are focused primarily on the control action pathway of a system. In contrast, the information feedback pathway lacks unsafe control actions (UCAs), which are typically related to software basic events; thus, assessment of software basic events in such systems is unclear. In this work, we present the idea of intermediate processors and unsafe information flow (UIF) to help safety analysts trace failure mechanisms in the feedback pathway and how they can be integrated into a fault tree for improved assessment capability. The concepts presented are demonstrated in two comprehensive case studies, a smart sensor integrated platform for unmanned autonomous vehicles and another on a representative advanced human system interface (HSI) for safety critical plant monitoring. The qualitative software basic events are identified, and a fault tree analysis is conducted based on a modified Redundancy-guided Systems-theoretic Hazard Analysis (RESHA) methodology. The case studies demonstrate the use of UIF and intermediate processors in the fault tree to improve traceability of software failures in highly complex digital instrumentation feedback. The improved method can also clarify fault tree construction when multiple component dependencies are present in the system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Failure Mechanism Traceability and Application in Human System Interface of Nuclear Power Plants using RESHA

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

EMI Mitigation of a &Cacute;uk-Based Power-Electronic System Using Switching-Sequence-Based Control

Switching-sequence-based control (SBC) laws when designed based on topological switching behavior can have positive effects on slow- and fast-scale dynamics of a power-electronic system (PES). The slow-scale control can encompass fast PES state regulation and tracking, based on predefined objective, while fastscale control can address differential-mode (DM) and commonmode (CM) spectral-peak energy associated with PES switching operation. Such control laws may offer enhanced programmability to conventional PES design where bulky electromagnetic interference (EMI) filters have been traditionally used to reduce EMI of switching power converters to meet EMI regulatory standards. An EMI filter is always a less programmable solution since it is usually designed for the worst-case EMI mitigation and usually overkill for a PES operating under reduced load condition. The control scheme outlined in this article offers EMI mitigation across wide operating regions without compromising PES regulation. Moreover, it does so by use of switching sequences that guarantee the reachability of the PES dynamics using an advanced Lyapunov-function-based approach. SBC is a powerful tool to generate control actions for a PES based on multivariate PES state constraints. Hence, contemporary EMI regulatory standards are used as constraints in the SBC formulation to operate the PES under wide operating regime while autonomously mitigating the EMI levels. The work may be of paramount importance for operating the ultra-fast-transition recent wide-bandgap semiconductor devices like GaN–FET and SiC MOSFET under higher power with increasing switching frequencies, which is usually desirable for increased power density and reduced switching losses. Here, a hardware Cuk–PES operated ´ with GaN–FETs is fabricated and is used for case illustration. It is shown by experimental results how SBC mitigate DM and CM EMI noise of the PES while maintaining regulation even for the higher order nonminimum phase PES, while reducing sensor requirements using state observer derived from the switching model of the PES.

42 ENGINEERING↗

DOE OSTI2021

Characterization of Embedded Sensors in Stainless Steel Test Articles and Design/Planning for MAGNET Testing

The nuclear industry is pursuing microreactors that can be factory assembled and deployed to remote regions for reliable power generation. One class of microreactors uses a monolithic metal core block coupled to heat pipes for heat rejection, which results in significant thermal stresses in the monolithic structures. This work describes the initial characterization and test plan for evaluating stainless steel test articles fabricated with embedded sensors for measuring heat pipe performance limits, as well as spatially distributed temperatures and strains during electrically heated thermal testing. The electrically heated testing will be performed in the non-nuclear Microreactor Agile Non-Nuclear Testbed and Single Primary Heat Extraction and Removal Emulator facilities located at Idaho National Laboratory. The goals of these tests are to (1) accurately monitor temperature and strain distributions that result from differential thermal expansion in the test articles and (2) quantify heat rejection limits of heat pipes as a function of operating temperature and working fluid during steady-state and transient operations. More generally, the ability to monitor component and system health during microreactor operation is attractive for providing a high sensor density to inform a limited number of microreactor operators to ultimately reduce operation and maintenance costs and move toward semi-autonomous operation. This report discusses the characterization of embedded thermocouples and fiber optic sensors in relevant test articles, including cylindrical pipes and hexagonal monolithic test articles for heat pipe-based reactors. The sensors were embedded by placing them in machined channels and then building up additional material by using ultrasonic additive manufacturing (UAM). UAM is a solid-state welding process that uses downward pressure and a lateral scrubbing motion to bond thin metal foils to a base material layer by layer. The ultrasonic welding process relies on the plastic deformation of the metal—as opposed to typical melting and solidification—to break oxide scales and bond the metal layers. The characterization of these embedded sensors included evaluating fiber optic signal attenuation, observing residual strain in the fibers, investigating microstructural and mechanical aspects, and demonstrating the sensors under various thermal loads and acoustic vibrations. Post-embedding characterization showed a fine grain structure (<1 μm) near the interfaces of the bonded foils as a result of severe deformation from the welding process. A large increase in hardness was observed at the foil interfaces and the fiber/matrix interface compared with the bulk matrix. Even when compared with the SS304 interfaces, the higher hardness observed around the embedded fiber suggests a higher degree of deformation due to the soft metal coating around the silica fiber core. The distributed fiber-optic temperature sensors and embedded thermocouples reliably measured temperature distributions during steady-state and transient thermal testing. The embedded fiber-optic sensors reliably measured strain during both transient and steady-state testing and properly identified resonant frequencies during acoustic testing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Decentralized Microgrid Protection Through Relative Fault Direction Classification: Preprint

Protection in inverter-based resources (IBRs) dominated microgrids generally face significant challenges due to the low fault current and inconsistent fault behaviors from IBRs. Recently, machine learning-based approaches have attracted considerable attention to address these challenges. This paper introduces a novel decentralized protection strategy for microgrids. The proposed method decomposes the protection challenge into several distributed learning tasks, enabling individual relays to autonomously determine the direction of faults using a binary classification framework based on support vector machine (SVM) algorithms. Following the distributed fault direction estimation, classifier outcomes are shared among neighboring relays, facilitating a local decision-making process to ascertain the presence of faults within the neighborhood. Finally, a tripping signal is generated based on the classifier results of each relay to operate the circuit breaker. To test and validate this approach, a 100% renewable microgrid model is simulated in MATLAB/Simulink. In the numerical analysis, the application of SVM classifiers in our approach yields impressive results: an average relay classification accuracy of 98%, and a 96% accuracy in circuit breaker control. These findings highlight the potential of machine-learning-based approaches in enhancing the efficiency and reliability of microgrid protection systems.

decentralized algorithm↗

DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗

DOE OSTI2022

Real-Time Ecodriving Control in Electrified Connected and Autonomous Vehicles Using Approximate Dynamic Programing

Connected and automated vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the ecodriving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multilayer model predictive control (MPC) approach for improving the fuel economy of a 48 V mild-hybrid powertrain in a connected vehicle environment. Approximate dynamic programing (DP) is used to solve the receding horizon optimal control problem, whose terminal cost is approximated with the base policy obtained from the long-term optimization. The controller was tested virtually (with deterministic and Monte Carlo simulation) across multiple real-world routes, demonstrating energy savings of more than 20%. The controller was then deployed on a test vehicle equipped with a rapid prototyping embedded controller. In-vehicle testing confirm the energy savings obtained in simulation and demonstrate the real-time ability of the controller.

Automation & Control Systems↗

DOE OSTI2026

Autonomous Hydrogen Fueling Station

This project “Autonomous Hydrogen Fueling Station” covered the autonomous refueling with both gaseous hydrogen and liquid hydrogen. The part on gaseous hydrogen focused on the development of an autonomous robotic fueling arm that would couple to a fuel cell engine for hydrogen refueling without guidance from the forklift operator and budget period. Research was also covered for the robotic fueling with a commercial vehicle. The second phase of the project created the baseline for an autonomous liquid hydrogen transfer system that would minimize boil off losses by operating at thermodynamically efficient state points. For the development of the robotic fueling arm, testing was conducted to establish a baseline measurement of the accuracy and repeatability of a human operator positioning a lift truck in front of a dispenser. The goal was to establish the range of motion required for an autonomous fueling mechanism to mate a hydrogen nozzle with a receptacle on a fuel cell system installed in a forklift. The final design comprised a selective compliance articulated robot arm (SCARA)-type mechanism with two arms for horizontal motion and a ball screw for vertical movement and color and LIDAR cameras were used for marker identification and proximity awareness to guide the robotic arm to its target receptacle. Initial tests resulted in 199 out of 200 successful attempts at autonomous coupling of the dispensing coupler and a fuel cell engine, without hydrogen. The dispenser prototype was modified to include tubing for both hydrogen fuel and air purge lines, but subsequent tests were confounded by the shoulder motor over current errors which limited the robot from getting to the fully inserted position to achieve a positive latch. Robotic hydrogen refueling was successfully demonstrated over 1.5 hours of testing, Plug completed 29 successful latches with an average number of 4 sequential latches before failure. However, a robot capable of placing the nozzle with more force is required for higher reliability. For budget period two, a small scale (10 kg / transfer) automated control system was designed that would operate valves to control pressure and flow of liquid nitrogen between a source and receiving tank with an aim to minimize boil off losses by operating at the most thermodynamically efficient state points. Control system logic flow and a P&ID were developed prior to system safety characterization via HAZOP. A control narrative and system state points were defined. Delays in approval for a change of project objective and procurement issues precluded the construction and test of the final prototype system.

08 HYDROGEN↗

DOE OSTI2024

Solid State Quantum Refrigeration Superconducting, Absorption and Measurement Based (Final Technical Report)

During this DOE grant, DE-SC0017890, in place for the past six years, all proposed research was carried out and published in peer-reviewed papers, as well as other projects that emerged during the research. In that effort the research team accomplished all proposed research, as well as many closely related research projects discovered and conceived of during the grant. These works included “Efficient Quantum Measurement Engines”, a work published in Physical Review Letters, giving a theory of quantum measurement-based engines, which uses quantum measurement as a resource. These engines are designed to efficiently convert energy from the stochastic quantum measurement process into useful work. Further publications include “Experimental Realization of a Quantum Dot Energy Harvester”, a joint theory and experimental work in collaboration with the group of Charles Smith in Cambridge, UK, as well as long time theoretical collaborators, Rafael Sánchez and Björn Sothmann. This work, featured as an Editor’s Suggestion in Physical Review Letters, realized an earlier theoretical proposal of ours, whereby two resonant tunneling quantum dots are connected to a central electronic cavity that is heated by a hot energy source. We also published “Superconducting Quantum Refrigerator: Breaking and Rejoining Cooper Pairs with Magnetic Field Cycles” a work done in collaboration with experimentalist Francesco Giazotto from ENS Pisa, Italy, which also resulted in a patent. This paper, published in Phys. Rev. Applied, advanced the concept of a cyclic fridge based on the normal/superconducting phase transition together with layered materials separated by tunnel junctions. We also completed the proposed research on a heat transistor, publishing “Thermal transistor and thermometer based on Coulomb-coupled conductors”, carried out as a collaboration between my group and theorists Splettstoesser (Lund U., Sweden), Sothmann (U. Duisburg-Essen, Germany), and Sánchez (U. Autónoma de Madrid, Spain). We carried out an analysis of a quantum coupled to a quantum point contact as a sensitive thermometer and heat transistor. We found the optimal statistical estimator for the temperature and compared it with experiments on the same type of devices. We also investigated autonomous quantum absorption refrigerators using quantum dots to cool by using a very hot thermal reservoir to drive heat between two other reservoirs. In the article “Quantifying the quantum heat contribution from a driven superconducting circuit”, we demonstrated that for a driven superconducting circuit, we showed heat flow provided by a hot source to the qubit can be switched on and off by varying external parameters, the frequency and the intensity of the driving. In the work “Stochastic thermodynamic cycles of a mesoscopic thermoelectric engine”, we reconsidered the autonomous thermoelectric heat engine in terms of underlying cycles. Rather than periodic behavior, the cycles were stochastic in nature. Nevertheless, by undertaking a graph theoretical analysis of the elementary transport processed, great quantitative and qualitative insight could be found. We also considered the quantum measurement process and showed that a quantum version of Maxwell’s demon could be related to the work extraction of a quantum system, closely related to arrow-of-time measures for quantum measurement, as described in our article “Thermodynamics of quantum measurement and Maxwell's demon's arrow of time”. This work was selected in Phys. Rev. A as an Editor’s Suggestion. A recent preprint titled “Cyclic Superconducting Quantum Refrigerators Using Guided Fluxon Propagation” accomplished an important piece of this grant: to propose a new kind of quantum refrigerator using the dynamics of fluxons in a type II superconductor. This invention envisioned a race-track type geometry where fluxons are confined. By applying a gradient of magnetic field together with electric current in a Corbino geometry, the circulating fluxons can actively cool a cold reservoir, realizing a new type of cyclic superconducting refrigerator. We also investigated the possibility of thermal control from different points of view. The application of quantum measurement to the system gives a new kind of control on the system of interest – we have pioneered this approach and shown that measurement can boost the thermal power of quantum engines as described in “Continuous measurement boosted adiabatic quantum thermal machines”. The ability to have heat flows on demand is an outstanding challenge, and we have provided new solutions to this problem in Thermal control across a chain of electronic nanocavities” for a chain of electron cavities using gating voltage control. The control methods using qubit/qubit coupling to create absorption fridges at their most fundamental level have also been developed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Crystal generation using the fully differentiable pipeline and latent space optimization

We present a materials generation framework that couples a symmetry-conditioned variational autoencoder with a differentiable SO(3) power spectrum objective to steer candidates toward a specified local environment under the crystallographic constraints. In particular, we implement a fully differentiable pipeline that performs batch-wise optimization on both direct and latent crystallographic representations. Using the GPU acceleration, the implementation achieves about fivefold speed compared to our previous CPU workflow, while yielding comparable outcomes. In addition, we introduce the optimization strategy that alternatively performs optimization on the direct and latent crystal representations. This dual-level relaxation approach can effectively escape local minima defined by different objective gradients, thus increasing the success rate of generating complex structures satisfying the target local environments. This framework can be extended to systems consisting of multi-components and multi-environments, providing a scalable route to generate material structures with the target local environment.

conditional VAE↗

DOE OSTI2024

An Autonomous Critical Data Extrapolator for the AGN-201m

Nuclear nonproliferation serves as a key goal, being undertaken by the International Atomic Energy Agency (IAEA). To recognize proliferation there are two pathways that states, who intend to use nuclear material for malicious purposes can take, diversion can misuse. Diversion is when fissile nuclear material is declared to the IAEA for non-weapon purposes, but then covertly removed. If the source of nuclear material, that is not declared and not fissionable, is placed inside the reactor core to create fissile material used to create weapons then the state is using the second pathway of proliferation, misuse. With the emerging development in areas of simulation and machine learning the creation of virtual models of reactor systems, digital twins, serve as a potential method to identify proliferation through detecting anomalous behavior in the reactor. A digital twin for a physical nuclear reactor has never been developed, as digital twins serve as an emerging technology. To investigate the process for development and use of a digital twin for a nuclear reactor Idaho State University’s AGN-201m serves as the nuclear reactor used for development of this digital twin. A data acquisition system has been installed to the reactor system allowing for the transfer of collected data from a reactor operation to Idaho National Laboratory’s Deeplynx data warehouse. When utilizing data to train reactor physics and machine learning models, a significant challenge encountered is the initial state of the data. Nuclear proliferation will have the capacity to be detected when the reactor immediately starts up, nor will it occur after the reactor shuts down. Generally, it will be detected when the reactor is operating at some desired power over a sufficient period for that specific reactor design. For the AGN-201m this will be when the reactor is critical (generally 1 mW or above) for a timespan that is within or less than the range of a regular business day. Datasets sent to Deeplynx have had to be manually cut to when the reactor is critical based on plots of power levels. This method is inefficient and laborious, especially when using multiple datasets at once to train a model. To provide a more streamlined approach an automated critical data extrapolator is developed, with capabilities of recognizing when the reactor operation first reaches criticality, and when the reactor undergoes a SCRAM and is shutdown.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Optimize the Autonomous Sensor Fish Device for Understanding Interactions of Aquatic Animals with Marine and Hydrokinetic and Hydro Systems (CRADA 476) Final Report

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.

13 HYDRO ENERGY↗

DOE OSTI2025

Predicting Pulsed-Laser Deposition SrTiO 3 Homoepitaxy Growth Dynamics Using High-Speed Reflection High-Energy Electron Diffraction

Pulsed-laser deposition (PLD) is a powerful technique for growing complex oxides with controlled stoichiometry. To understand growth dynamics therein, it is common to leverage in situ spectroscopies, such as reflection high-energy electron diffraction (RHEED), to monitor surface crystallinity. Most commercial systems rely on video-rate cameras operating at 60-120 Hz that lack sufficient temporal resolution to capture growth dynamics at practical deposition frequencies. Here, a high-speed platform to record in situ dynamics via RHEED at >500 Hz is implemented. An open-source analysis package is designed to fit diffraction spots to 2D Gaussians, allowing single-pulse surface reconstruction kinetics extraction. Using homoepitaxially deposited (001)-oriented SrTiO 3 as a model system, we demonstrate how high-speed RHEED can provide real-time insight into growth processes obscured by slower acquisition systems. By fitting the single-pulse intensity to a set of exponential functions, we observe changes in the characteristic decay time and mechanism correlated to the substrate step width and surface termination. We observe distinct surface effects, with diffraction intensity decaying on lower-energy TiO 2 -terminated surfaces and stabilizing on SrO- or mixed-terminated surfaces. Similarly, using an exponential model, the extracted characteristic time of adatom deposition decreases with increased density of bonding sites associated with mixed termination and narrower step widths. Ultimately, this work shows how increasing RHEED temporal resolution can uncover new insights into growth processes, with practical implications for the design and control of PLD processes. This experimental platform provides new capabilities to enable data-driven machine learning analysis and autonomous control systems to enhance the complexity and fecundity of PLD.

(SrO)↗

DOE OSTI2019

SPARR: Spiking/Processing Array for Wide Dynamic Range and High Resolution Photonic Sensing

The Spiking/Processing Array (spARR) is a novel photonic focal plane that uses pixels which generate electronic spikes autonomously and without a clock. These spikes feed into a network of digital asynchronous processing elements or DAPES. By building a useful assemblage of DAPES, and connecting them together in the correct way, sophisticated signal processing can be accomplished within the focal plane. Autonomous self-resetting pixels (AsP) enable SPARR to generate electronic response with very small signals--as little as a single photon in the case of Geiger mode avalanche photodiodes to as few as several hundred photons for in-cmos photodetectors. These spiking pixels enable fast detector response, but do not draw as much continuous power as synchronous clocked designs. The spikes emitted by the pixels all have the same magnitude, the information from the scene is effectively encoded into the rate of spikes and the time at which the spike is emitted. The spiking pixels, having converted incident light into electronic spikes, supply the spikes to a network of digital asynchronous processors. These are small state machines which respond to the spikes arriving at their input ports by either remaining unchanged or updating their internal state and possibly emitting a spike on one or more output ports. We show a design that accomplishes the sophisticated signal processing of a Haar spatial wavelet transform with spatial-spectral whitening. We furthermore show how this design results in a data streams which support imaging and transient optical source detection. Two simulators support this analysis: SPICE and sparrow. The CMOS SPICE simulator Cadence provides accurate CMOs design with accounting for effects of circuit parasitics throughout layout, accurate timing, and accurate energy consumption estimates. To more rapidly assess larger networks with more pixels, sparrow is a custom discrete event simulator that supports the non-homogeneous Poisson processes that lie behind photoelectric interaction. Sparrow is a photon-exact simulator that nevertheless performs SPARR system simulator for large-scale systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Wave energy converter buoy for Arctic observations

Ocean observation buoys are currently powered-constrained by battery storage capacity or available solar power. Power constraints limit the number of measurements that can be made and the lifetime of the buoys. Ocean surface wave energy could be used for power production, but wave energy converters are not yet commercially available for ocean observation buoys. Here we present the design and testing of a drifting wave energy converter buoy using a pendulum transmission system (PTS). The wave energy converter buoy was designed to operate in Arctic temperatures and wave conditions, but it could be used in areas with warmer temperatures and larger waves. Field tests measured a maximum power production of 5 watts in waves with a significant wave height of 0.42 meters and a ten minute average power production of 37 milliwatts. Power production increased with the energy period and significant wave height. The energy harvesting capabilities of the PTS showed the utility of adding a wave energy conversion device to a drifting ocean observation buoy.

Arctic observations↗