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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Optimizing green infrastructure placement under precipitation uncertainty

Increased urbanization, infrastructure degradation, and climate change threaten to overwhelm stormwater systems across the nation, rendering them ineffective. Green Infrastructure (GI) practices are low cost, low regret strategies that can contribute to urban runoff management. However, questions remain as to how to best distribute GI practices through urban watersheds given precipitation uncertainty and the variable hydrological responses to them. We develop stochastic programming models to determine the optimal placement of GI practices across a set of candidate locations in a watershed to minimize the total expected runoff under medium-term precipitation uncertainties. Specifically, we first develop a two-stage stochastic programming model. Next, we reformulate this model using perturbed parameters to reduce the requisite computational time and extend it to multi-stage. In addition, we introduce constraints that allow for incorporating sub-catchment-level runoff reduction considerations. Furthermore, we account for hydrological connectivity in the watershed using an underlying acyclic connectivity graph of sub-catchments and incorporate various practical considerations into the models. In addition, we develop a systemic approach to downscale the existing daily precipitation projections into hourly units and efficiently estimate the corresponding hydrological responses. These advancements are brought together in a case study for an urban watershed in a mid-sized city in the U.S., where we perform sensitivity analyses, evaluate the importance of the considered constraints, and provide insights.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Thermal relaxation of strain and twist in ferroelectric hexagonal boron nitride moiré interfaces

New properties can arise at van der Waals (vdW) interfaces hosting a moiré pattern generated by interlayer twist and strain. However, achieving precise control of interlayer twist/strain remains an ongoing challenge in vdW heterostructure assembly, and even subtle variation in these structural parameters can create significant changes in the moiré period and emergent properties. Characterizing the rate of interlayer twist/strain relaxation during thermal annealing is critical to establish a thermal budget for vdW heterostructure construction and may provide a route to improve the homogeneity of the interface or to control its final state. Here, we characterize the spatial and temporal dependence of interfacial twist and strain relaxation in marginally-twisted hBN/hBN interfaces heated under conditions relevant to vdW heterostructure assembly and typical sample annealing. We find that the ferroelectric hBN/hBN moiré at very small twist angles (θ ≤ 0.1°) relaxes minimally during annealing in air at typical assembly temperatures of 170°C. However, at 400°C, twist angle relaxes significantly, accompanied by a decrease in spatial uniformity. Uniaxial heterostrain initially increases and then decreases over time, becoming increasingly non-uniform in direction. Structural irregularities such as step edges, contamination bubbles, or contact with the underlying substrate result in local inhomogeneity in the rate of relaxation.

2D materials↗
DOE OSTI2021

A Cyber-Physical System for Freeway Ramp Meter Signal Control Using Deep Reinforcement Learning in a Connected Environment

Freeway bottlenecks such as on-ramp merging areas account for about 40% of recurring freeway congestion. It is generally agreed that building more roads and adding more lanes to existing infrastructure does not solve the congestion problem, and so dynamic traffic control measures offer a more cost-effective alternative. Ramp meters, traffic signal devices that regulate traffic flow entering freeways, are among the most effective measures to mitigate congestion at on-ramp merging areas on freeways. The confluence of deep reinforcement learning (RL) and connectivity provides a possible solution to advance ramp meter signal control. Deep RL is a group of machine-learning methods that enables an agent learning from the environment to improve its performance. In this study, three deep RL methods-proximal policy optimization (PPO), Ape-X deep Q-network (DQN), and asynchronous advantage actor-critic agents (A3C)-are explored for ramp meter signal control to maximize vehicle speed and traffic throughput, as well as to minimize energy consumption and emissions at freeway on-ramp merging areas in a connected environment. The low computational requirement and scalability of deep RL for deployment make it a powerful optimization tool for time-sensitive applications such as ramp meter signal control. The results of this study show that deep RL methods yield superior performance to both a fixed-time controller and ALINE A, a state-of-the-art feedback controller.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2020

An intra-node HPC network architecture with nanosecond-scale photonic switches

We propose a single-stage network architecture for intra-node connectivity that makes use of nanosecond-scale photonic switches. Although buffering at the switch points is of vital importance for complex multi-stage networks, this is not the case for smaller-scale single-stage networks where the end nodes are located only one hop apart. By limiting the buffering to the end points, the proposed architecture manages to minimize the required electro-optic and opto-electronic conversions, leading in this way to both low end-to-end latency and better energy efficiency. Combining these advantages with nanosecond-scale switching times can allow for high-throughput operation even for frequent switch reconfigurations. The performance of the proposed architecture is evaluated via discrete-event simulations for a wide range of synthetic-traffic cases. The simulation results show that high-throughput operation of ≥90% can be achieved even for small message sizes, i.e., 32 KB for all-to-all communication and 2 KB for uniform random traffic, at a data rate of 400 Gb/s and a switch reconfiguration time of ≤72 ns. Moreover, if 100-ns reconfiguration times are achievable as opposed to 150-ns, then for the all-to-all traffic case a 16% and 28% reduction in completion time can be achieved for message sizes of 8 KB and 1 KB, respectively. In a forthcoming era of optically interfaced processors and accelerators, nanosecond-scale photonic switches appear as a highly promising solution for keeping up with the intra-node bandwidth scaling due to their high-bandwidth, low-latency and fast-switching capabilities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Quantitative Risk Assessment for Hydrogen-Powered Locomotive Refueling

Hydrogen powered locomotives are being explored to reduce emissions in rail applications. The risks of operations like refueling should be understood to ensure safe environments for workers and members of the public. Sensitivity analyses were conducted using HyRAM+ to identify major drivers of risk and compare effects of system parameters on individual risk. The consequences of jet fires from full-bore leaks dominated the risk, compared to explosions or smaller leaks. Pipe size, leak detection capability, and leak frequencies of system components greatly affected risk while overpressure modeling parameters and ambient conditions had little effect. The effects of personal protective equipment (PPE) materials on individual risk were quantified by reducing the individual’s exposure time or absorbed thermal dose. PPE only showed a risk reduction in low-risk cases. This study highlighted target areas for risk mitigation, including leak detection equipment and component maintenance, and indicated that the minimal effects of other parameters on risk may not justify prescriptive requirements for refueling operators.

08 HYDROGEN↗
DOE OSTI2021

In Situ X-ray Measurements to Follow the Crystallization of BaTiO 3 Thin Films during RF-Magnetron Sputter Deposition

Here, we report on adding an important dimension to the fundamental understanding of the evolution of the thin film micro structure evolution. Thin films have gained broad attention in their applications for electro-optical devices, solar-cell technology, as well storage devices. Deep insights into fundamental functionalities can be realized via studying crystallization microstructure and formation processes of polycrystalline or epitaxial thin films. Besides the fundamental aspects, it is industrially important to minimize cost which intrinsically requires lower energy consumption at increasing performance which requires new approaches to thin film growth in general. Here, we present a state of the art sputtering technique that allows for time-resolved in situ studies of such thin film growth with a special focus on the crystallization via small angle scattering and X-ray diffraction. Focusing on the crystallization of the example material of BaTiO 3 , we demonstrate how a prototypical thin film forms and how detailed all phases of the structural evolution can be identified. The technique is shaped to enable a versatile approach for understanding and ultimately controlling a broad variety of growth processes, and more over it demonstrate how to in situ investigate the influence of single high temperature sputtering parameters on the film quality. It is shown that the whole evolution from nucleation, diffusion adsorption and grain growth to the crystallization can be observed during all stages of thin film growth as well as quantitatively as qualitatively. This can be used to optimize thin-film quality, efficiency and performance.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Flux Cube Reconstruction from Slitless Spectroscopy

Slitless spectroscopy enables efficient, large-area surveys without target preselection, yet it faces challenges from source blending, higher noise, and lost spatial–spectral information. We present an advanced, nonparametric, data-driven algorithm that leverages multiple dispersion angles to reconstruct three-dimensional flux distributions, providing low-resolution integral field unit capabilities from slitless data. By treating each pixel as an independent element, our method naturally handles source confusion without requiring prior assumptions regarding redshifts, templates, or model libraries. We validate the algorithm using simulated Roman Space Telescope wide-field slitless spectroscopy images that are equivalent to what is expected from the High-Latitude Time-Domain Survey. First, we demonstrate that a host-galaxy model reconstructed from multiple dispersion angles can be used to accurately subtract host light from a transient, recovering a Type Ia supernova spectrum with minimal bias. Second, we showcase a high-fidelity flux-cube reconstruction of a complex galaxy, successfully measuring the redshift and recovering continuum, emission, and absorption features. This approach highlights the potential of multi-dispersion-angle slitless data to provide spatially resolved spectral information in a nonparametric way, which is traditionally accessible only with integral field spectroscopy, opening a new window into large, unbiased, and spatially resolved studies of galaxy evolution.

Griggio, M. [Space Telescope Science Institute, Ba↗
DOE OSTI2025

Assessing Seismic Risk for CO2 Geologic Storage: Comparative Analysis of the Delaware Basin and Basin and Range Province Projects

ABSTRACT: Effective management of induced seismicity is critical for safe and sustainable CO2 storage. This study evaluates fault slippage risks in the Delaware Basin (Texas) and Basin and Range Province (Utah), integrating geological, operational, and geomechanical parameters to assess fault stability and seismic hazard mitigation. In the Delaware Basin, two sites were analyzed under an injection rate of 20,000 bbl/day over 25 years. One site showed low fault slip risk, while the other exhibited higher reactivation potential due to proximity to critically stressed faults. Sensitivity analysis revealed that increased pore pressure significantly heightened slip potential, highlighting the necessity of precise pressure control and real-time monitoring. In the Basin and Range Province, fault stability was evaluated at Neck of the Desert, Escalante Desert, Parowan, and Beaver sites under injection rates of 8,750 bbl/day per site over 30 years. Minimal fault slip risk was observed at Neck of the Desert and Escalante Desert sites, whereas Parowan and Beaver sites exhibited elevated slip potential due to semi-critically stressed faults sensitive to modest pore pressure increases. The findings demonstrate that fault slippage analysis, combined with sensitivity analysis of pore pressure and friction coefficients, is essential for understanding seismic risks. Continuous monitoring, adaptive injection management, and rigorous geomechanical analysis are key strategies for minimizing induced seismicity in CO2 sequestration projects.

58 GEOSCIENCES↗
DOE OSTI2025

Proactive Regulatory Approaches to Electrification and Load Growth: Workshop Report

On July 10 and 11, 2024, Pacific Northwest National Laboratory and RMI led a workshop in Aurora, Colorado, to explore novel and proactive approaches to electrification and load growth while minimizing risks and costs to customers. Over the next decade, a unique opportunity exists to invest strategically in the electricity system to enable electrification across the transportation, industrial, and building sectors and respond to data and technology-based load growth. However, current utility and regulatory planning practices are insufficient to identify and enable the right investments, and work must be done to reduce the risk and decisional uncertainty faced by utility regulatory commissions and utilities. Ensuring timely electrification investments may require new approaches to address risk, uncertainty, prudence, and cost recovery. Understanding the decision-making process and information needs of utilities and regulators is critical. New policies (or application of policies), financial tools, systems analysis, regulatory mechanisms, and enhanced process transparency may be required. The workshop's goal was to identify proactive regulatory approaches for electrification and load growth that minimize costs and risks to customers. Our intention was that the conversations and the resulting solutions and takeaways would be specific and tactical rather than general and theoretical and that together we would create actionable next steps for key actors in the system, including utilities, regulators, thought leaders, researchers, and the U.S. Department of Energy (DOE). This report is intended to provide workshop attendees with a record and summary of the discussion and proposals raised at the workshop and to provide interested entities who did not attend, such as other regulators, policymakers, utilities, and U.S. DOE offices, with an understanding of what was discussed and with ideas to explore in their organizations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Offloading Internet of Things (IoT) tasks to the cloud for further processing might not always lead to an optimal execution time, particularly in situations such as resource contention, under-provisioning, over-provisioning, and fragmentation. In addition, dynamically optimizing the number of Virtual Machines (VMs) for resource scheduling in order to meet application requirements remains a major research challenge. Further, existing resource scheduling algorithms focus primarily on minimizing operational costs while maximizing resource sharing and utilization. Considering energy utilization as part of the resource allocation and scheduling process as an optimization objective for maintaining load balancing has often been neglected. To address these challenges and more, we propose a cooperative energy-aware resource allocation and scheduling strategy based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. Here we used the Grid Workloads Archive dataset to evaluate our proposed approach named TOPREAL. Experimental results with respect to the allocation of VM resources when considering processing a large segment of tasks indicate that TOPREAL outperforms existing algorithms in terms of energy savings, with an average improvement of 40.25%, while maintaining an average improvement of 16.21% when it comes to execution time. Results also demonstrate that our method can save an average of 78.06 processing hours and 63,215kJ of energy when compared to existing scheduling algorithms. These results demonstrate the effectiveness of our proposed model and the viability of using multi-criteria decision-making techniques such as TOPSIS to solve the resource allocation and scheduling problem in edge environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Modified gravitational wave propagation with higher modes and its degeneracies with lensing

Low-energy alternatives to General Relativity (GR) generically modify the phase of gravitational waves (GWs) during their propagation. As detector sensitivities increase, it becomes key to understand how these modifications affect the GW higher modes and to disentangle possible degeneracies with astrophysical phenomena. We apply a general formalism — the WKB approach — for solving analytically wave propagation in the spatial domain with a modified dispersion relation (MDR). We compare this WKB approach to applying a stationary phase approximation (SPA) in the temporal domain with time delays associated to the group or particle velocity. To this end, we extend the SPA to generic signals with higher modes, keeping careful track of reference phases and arrival times. We find that the WKB approach coincides with the SPA using the group velocity, in agreement with the principles of wave propagation. We then explore the degeneracies between a GW propagation with an MDR and a strongly-lensed GW in GR, since the latter can introduce a frequency-independent phase shift which is not degenerate with source parameters in the presence of higher modes. We find that for a particular MDR there is an exact degeneracy for wave propagation, unlike with the SPA for particle propagation. For the other cases, we search for the values of the MDR parameters that minimize the χ 2 and conclude that strongly-lensed GR GWs could be misinterpreted as GWs in modified gravity. As a result, future MDR constraints with higher mode GWs should include the possibility of frequency-independent phase shifts, allowing for the identification of modified gravity and strong lensing distortions at the same time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Optimizing a chemical cleaning protocol to determine the nickel isotopic composition of ancient seawater from carbonates

Reconstructing the ancient marine cycle of Ni, a trace metal in the oceans essential to enzymes regulating the C, N, and O cycles, would inform how Ni bioavailability shaped early life. The Ni isotopic composition of seawater (δ 60 Ni SW ) through time could provide insight, but this approach requires sedimentary archives that faithfully record δ 60 Ni SW . Carbonates are an often-used record of seawater chemistry, but contaminants could bias the measured δ 60 Ni of bulk sediment. To isolate the true carbonate-bound δ 60 Ni signal, we optimized a chemical cleaning protocol designed to minimize non‑carbonate Ni contributions and maximize carbonate recovery. We analyzed ODP Site 1007 sediments and evaluated oxidative and reductive pre-cleaning steps and carbonate dissolution protocols. Our results show that only reductive cleaning significantly altered the subsequent carbonate leachate composition, lowering Ni/Ca ratios by up to 60% and δ 60 Ni values by as much as 0.4 ‰. In contrast, δ 60 Ni values were largely insensitive to premature carbonate dissolution during pre-cleaning or to partial leaching of silicates during carbonate dissolution, even at higher acid molarities or longer reaction times. We recommend an evidence-based protocol that removes hydro-soluble salts and clays, applies reductive cleaning and oxidative cleaning for certain sample compositions, and dissolves carbonate using 0.5 M acetic acid, adjusted to pH 5. Using these results, we infer a ∼ 0.9 ‰ Ni isotopic fractionation between chemically cleaned deep-water carbonates (+0.4 ± 0.1 ‰) and deep seawater. This method development is a key step toward accurately reconstructing ancient marine Ni cycles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

When Every Drum is a Win - Tackling a Thirty-Five Thousand Drum Legacy - 20064

Cameco Corporation's (Cameco) Port Hope Conversion Facility (PHCF) was previously owned by the federal Crown Corporation, Eldorado Nuclear, which held a significant inventory of legacy waste material at the time Cameco was formed in 1988. As a result, Cameco was granted an allocation of 150,000 cubic metres of space in the Long-Term Waste Management Facility (LTWMF) located in the Municipality of Port Hope (MPH), which opened to the receipt of Cameco material in June 2018. Cameco is currently undertaking a major site cleanup and renewal of its Port Hope Conversion Facility (PHCF) which is known as the Vision in Motion (VIM) project. Over its operating history, Eldorado accumulated an inventory of over 35,000 drums of accumulated waste that was primarily stored at two offside warehouse locations. In 2017 and 2018, the project focused on repackaging two well characterized legacy wastes (magnesium fluoride slag and depleted uranium titanium oxide) from one of the two offsite locations. At the conclusion of this work, approximately 15,000 drums of these wastes had been repackaged and disposed of at the LTWMF. There remained approximately 1000 drums from this location that either could not be repackaged safely or were different waste types and required further verification and/or processing. The buildings at this location were scheduled for demolition in early 2019, which triggered the development of a process for triaging legacy drums in August 2018, which allowed for preparation and shipment of approximately half of these drums to the LTWMF by March 2019, with the remainder moved to the second offsite warehouse location in February 2019 for further characterization which is ongoing. This paper will discuss key lessons learned as the drummed legacy waste disposal inventory has been reduced to approximately half of the initial inventory. This will include the development of an alternate packaging process; prioritization of characterization activities and selection of techniques where minimal inventory information is available; determination of next step(s) for each drum as it is assessed; key safety considerations; and how to make inroads into an overwhelming task while under public and regulatory scrutiny. In a relatively short period of time, significant progress has been made to organize and gather information about the legacy waste, update the inventory records and determine the most appropriate pathways (i.e. LTWMF disposal, disposal at another appropriate facility, site storage until future processing and/or disposal). Since 2017 the volume of the decades-old legacy waste inventory has decreased significantly. With the majority of the known materials safely disposed of at the LTWMF, every drum removed from the endless rows of 20,000 pyramidally-stacked waste drums with limited history that is safely characterized, prepared and shipped is considered a win. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

A practical approach to determine minimal quantum gate durations using amplitude-bounded quantum controls

Here, we present an iterative scheme to estimate the minimal duration in which a quantum gate can be realized while satisfying hardware constraints on the control pulse amplitudes. The scheme performs a sequence of unconstrained numerical optimal control cycles that each minimize the gate fidelity for a given gate duration alongside an additional penalty term for the control pulse amplitudes. After each cycle, the gate duration is adjusted based on the inverse of the resulting maximum control pulse amplitudes by re-scaling the dynamics to a new duration where control pulses satisfy the amplitude constraints. Those scaled controls then serve as an initial guess for the next unconstrained optimal control cycle, using the adjusted gate duration. We provide multiple numerical examples that each demonstrate fast convergence of the scheme toward a gate duration that is close to the quantum speed limit, given the control pulse amplitude bound. The proposed technique is agnostic to the underlying system and control Hamiltonian models, as well as the target unitary gate operation, making the time-scaling iteration an easy to implement and practically useful scheme for reducing the durations of quantum gate operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fabrication of Arrays of Topological Solitons in Patterned Chiral Liquid Crystals for Real-Time Observation of Morphogenesis

Topological solitons have knotted continuous field configurations embedded in a uniform background, and occur in cosmology, biology, and electromagnetism. However, real-time observation of their morphogenesis and dynamics is still challenging because their size and timescale are enormously large or tiny. Liquid crystal (LC) structures are promising candidates for a model-system to study the morphogenesis of topological solitons, enabling direct visualization due to the proper size and timescale. We report a new way is found to rationalize the real-time observation of the generation and transformation of topological solitons using cholesteric LCs confined in patterned substrates. The experimental demonstration shows the topologically protected structures arise via the transformation of topological defects. Numerical modeling based on minimization of free energy closely reconstructs the experimental findings. The fundamental insights obtained from the direct observations pose new theoretical challenges in understanding the morphogenesis of different types of topological solitons within a broad range of scales.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Direct numerical simulations of turbulent reacting flows with shock waves and stiff chemistry using many-core/GPU acceleration

Compressible reacting flows may display sharp spatial variation related to shocks, contact discontinuities or reactive zones embedded within relatively smooth regions. The presence of such phenomena emphasizes the relevance of shock-capturing schemes such as the weighted essentially non-oscillatory (WENO) scheme as an essential ingredient of the numerical solver. However, these schemes are complex and have more computational cost than the simple high-order compact or non-compact schemes. In this paper, we present the implementation of a seventh-order, minimally-dissipative mapped WENO (WENO7M) scheme in a newly developed direct numerical simulation (DNS) code called KAUST Adaptive Reactive Flows Solver (KARFS). In order to make efficient use of the computer resources and reduce the solution time, without compromising the resolution requirement, the WENO routines are accelerated via graphics processing unit (GPU) computation. The performance characteristics and scalability of the code are studied using different grid sizes and block decomposition. Furthermore, the performance portability of KARFS is demonstrated on a variety of architectures including NVIDIA Tesla P100 GPUs and NVIDIA Kepler K20X GPUs. In addition, the capability and potential of the newly implemented WENO7M scheme in KARFS to perform DNS of compressible flows is also demonstrated with model problems involving shocks, isotropic turbulence, detonations and flame propagation into a stratified mixture with complex chemical kinetics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗