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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Facility Improvements and Modifications to Extend the Operational Life of the Modular Caustic-Side Solvent Extraction Process - 16082

Savannah River Remediation, LLC (SRR) is working to remove, stabilize, and dispose of approximately 36.7 million gallons (138.9 million liters) of liquid radioactive waste currently stored in 43 underground waste tanks, and ultimately close waste tanks. In 2008, as an interim salt treatment system to remove cesium from high level waste salt solutions prior to the Salt Waste Processing Facility start up, the Modular Caustic-Side Solvent Extraction Unit (MCU) initiated the removal of cesium from liquid radioactive salt waste prior to disposal. The MCU was designed and built for an expected three-year operational life and five-year design life. The first MCU cold runs began in June 2007 and hot startup was completed in April 2008. MCU remains in hot operation to this day, aiding in multiple, high-level tank closures. In an effort to continue liquid waste disposition at the Savannah River Site (SRS), equipment upgrades and operational improvements have been made to extend the life of the MCU beyond the original three years. Several pieces of fixed equipment have been converted into removable equipment which reduces the downtime associated with equipment change-outs and thereby reduces the dose to workers and processing impacts. Multiple pumps and filters have been upgraded to more robust designs. Process monitoring and controls have been upgraded to improve process performance. Redundant centrifugal contactor vibration equipment has been installed on each contactor in order to better assess the reliability, processing performance, and lifetime of the contactors. A new Solvent Hold Tank (SHT) Sampler has been installed to minimize the organic cross-contamination within the sampler, dose to workers, and sample analysis time. To increase processing availability and aid in visually obtained data, camera port has been added to the Salt Solution Feed Tank (SSFT). In order to decrease the overall background dose to the workers, the Process Vessel Ventilation (PVV) duct work now has the capability to be flushed to remove contamination. The caustic wash addition system has been modified to be a once-through system to improve equipment reliability, decrease downtime, and decrease dose to workers. In addition, the solvent that MCU utilizes to extract Cesium out of salt waste has been upgraded to Next Generation Solvent (NGS) which is based on MaxCalix extractant instead of a BobCalix extractant. Further, these upgrades and improvements have allowed MCU to be able to process at higher flow rates (up to 8.5 GPM) and with increased throughput (as high as 1.3 million gallons in FY13 and 5.3 million gallons since hot operations initiated) that have allowed SRR to continue the disposition of nuclear salt waste. The continued operation of MCU ensures more curies are stabilized into a glass matrix at SRS's Defense Waste Processing Facility (DWPF) and less curies are retained within South Carolina at SRS's Saltstone Disposal Facility (SDF). Unique challenges associated with modifying and improving a demonstration plant that is in hot operations and was designed to be temporary with a limited plant footprint have been successfully overcome. Further, creative strategies for removing old equipment have been utilized given the limited decontamination agents available for use that will not interfere with the organic solvent. This paper presents and discusses the deployment of these upgrades and operational improvements to MCU in order to allow continued, increased salt waste processing beyond the initial design life. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Regulating Operational Safety at a Future Geological Disposal Facility in the UK - 18038

The United Kingdom (UK) has generated commercial nuclear energy for over 60 years; generating a significant and varied inventory of higher activity radioactive waste (HAW). In accordance with international practice, UK Government policy for long-term management of HAW is disposal in a geological disposal facility (GDF). The nuclear materials comprising HAW include high level waste, intermediate level waste and some low level waste, not suitable for disposal to the Low Level Waste Repository. In addition, other nuclear materials may be reclassified as waste in the future and will require to be managed as HAW for disposal to a GDF; these include spent fuel, uranium and plutonium. The operator of a GDF will be required to obtain a nuclear site licence from the Office for Nuclear Regulation (ONR), as the nuclear safety and security regulator, in order to construct and operate the facility. In the period prior to submission of a licence application, ONR is working to develop its regulatory expectations and provide advice on its regulatory role with regards to a future GDF. ONR has developed regulatory guidance on operational safety, benchmarked against the relevant IAEA Safety Standards and the Western European Nuclear Regulators Association (WENRA) Safety Reference Levels for Disposal. This guidance draws upon ONR's current expectations for nuclear sites; including operational reactors, fuel manufacture and reprocessing facilities, and reactors undergoing decommissioning; and incorporates relevant good practice drawn from international programmes for geological disposal and related industries (eg mining). In addition to the technical assessment guidance, ONR has developed guidance on the licensing process specific to a GDF; providing clarity for example on when a licence will be required. A review of the standard licence conditions applied to all nuclear sites concluded no significant amendment would be required for application to a GDF. Through its voluntary agreement with Radioactive Waste Management Limited (RWM), the developer and future operator of a GDF in the UK, ONR exerts regulatory influence on the design and organisational development of the future licensee. As part of this scrutiny, ONR has assessed RWM's generic Disposal System Safety Case. Subjecting the generic design and safety case to regulatory review provides opportunity for early engagement to ensure the appropriate standards will be achieved. ONR will only grant the necessary regulatory permissions if RWM adequately demonstrates the GDF will be constructed, operated and closed safely. Future challenges faced by ONR with respect to a GDF include building and maintaining regulatory competence and confidence, and engaging effectively with the public as the UK begins its search for a suitable site to provide appropriate assurance that a GDF will be operated safely and securely. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

A Practical Approach of Mining Operational Risks in Cigeo - A field-test - 19349

Andra is currently completing the detailed engineering phase of the stepwise design development of Cigeo (the French HLW and IL-LLW Deep Geological Repository, aka DGR). Various technical issues are at stake, including the risk management requirements linked to the safety of 'Mining Operations' (i.e. occupational safety issues related to underground excavation/dismantling works at some 500 m of depth), at different stages of the DGR life cycle (i.e. from its construction start-up to its final closure and future monitoring/or conversely a decision of waste retrieval). These occupational safety requirements have a significant impact on Cigeo's design, its progressive construction and later closure. On a shorter term, one must demonstrate that the design bases are technically sound and that the solutions selected (to build, operate, close the DGR infrastructures or conversely retrieve the waste) are both feasible and safe. In that sense (and following an identification of operational risks during construction), Andra focused on two sensitive cases: - The construction of large diameter drift crossings, in particular those related to the connection between a transfer drift (main) and a IL-LLW disposal vault (i.e. during construction operations), - The partial dismantling of a drift concrete lining (made of concrete segment rings, erected and placed by a TBM) at time of construction of a horizontal seal (i.e. during closure operations). The discussion starts with a description of the geotechnical conditions prevailing at the (French) Bure site, in the Callovo-Oxfordian (COx) claystone formation. Then the paper presents the Cigeo underground architecture (at large) and the specific structural choices made (at the end of the detailed engineering phase) for drifts, disposal vaults and seals. The discussion on construction - deconstruction challenges for each of the critical issues cited above provides an opportunity to explain the operational sequences (and how the methodology selected enables to reduce the risks). The following part of the paper focuses the technological demonstration (an in-situ test to be planned in detail) of a partial dismantling of the concrete segments rings forming the support lining of two tunnels previously built in the Meuse/Haute-Marne URL (Underground Research Laboratory) infrastructures, combined with the construction of a specific drift crossing. In this dismantling and construction test, the technical demonstration objective is three-fold: - The capacity to lay down compressible arch segments and support (by bolts and mesh) the rock after the lining removal, - The capacity to excavate and line the drift crossing, - The structural / geomechanical behavior (evolution with time) of the rock and lining compared with that of 'neighboring' zones. This paper provides a timeline of the tests. Should the results obtained deliver some positive perspectives of technical feasibility (and convincing operational safety) Andra will incorporate this dismantling/construction solution in the 'design freeze' of the DGR underground infrastructures. The positive outcomes of this technical test campaign will also provide 'elements of proof' for the instruction of the Cigeo licensing (a process likely to take place between 2020 and 2024) by the French Nuclear Safety Authority (ASN). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

On-Line Monitoring to Detect Sensor and Process Degradation under Normal Operational Transients - 327

Calibration assessment of nuclear plant sensors and transmitters is currently performed through periodic maintenance actions during plant refueling outages. On-line monitoring (OLM) can be used to reduce the time and cost of this maintenance process by using empirical models to evaluate sensor calibration. The implementation of OLM can also mitigate human error associated with unnecessary maintenance on sensors operating within calibration tolerances. OLM has been studied and validated for plants under steady-state baseload operation, but the extension to load-following operation is not well-documented in the literature. Auto-associative kernel regression (AAKR) provides an error-corrected prediction of process sensor measurements for OLM through a weighted average of historic data. The current study evaluates the performance of AAKR for prediction under normal operational transients (such as expected under load-following operation). Performance of the AAKR model with two anomaly detection routines, error uncertainty limit monitoring (EULM) and sequential probability ratio test (SPRT), was evaluated for normal operation, sensor faults, and process degradation. The data used to develop and evaluate the OLM system were collected from a forced flow loop with temperature and flow sensors. Three models of well-correlated groups of sensors are trained to predict normal system behavior under planned transients. The anomaly detection routines are tested for sensor faults and process faults. The results of this experimental study suggest AAKR and an appropriate anomaly detection routine can be applied for sensor calibration and process fault detection under transient operation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

An Efficient Annual-Performance Model of a Geothermal Network for Improved System Design, Operation, and Control: Preprint

Geothermal district energy systems (DES), and specifically geothermal networks, provide a viable solution for decarbonizing residential and commercial heating and cooling loads. District energy systems of all kinds enable a thermal resource with a relatively high capital cost (such as a geothermal borehole field) to be shared among a large number of users. While district heating and cooling has been studied for many years, geothermal networks, fifth generation DES that utilize water-source heat pumps and an ambient temperature loop to meet heating and cooling loads, have not been implemented extensively and thus require additional technical and economic optimization to obtain maximum benefits. This paper presents a newly developed reduced-order model that captures the flow of energy within the network, including the commercial and residential users' electrical usage, at an hourly rate over a year. The model includes building loads, heat pumps, borehole fields, and auxiliary heat/cool input, all connected with an ambient-temperature thermal loop model. In the model, operational control is possible for the borehole fields, circulation pump, and auxiliary system. For a given system, the model can output the complete state parameters for each component, the thermal loop, and the collective system, such as flow rate over time, average thermal loop temperature over time, and total electricity usage. The model can also be used to optimize the system control for maximizing system efficiency or minimizing system operational cost. For example, one initial assessment of the borehole controller for an example system showed that a controller with on/off operation of the borehole field reduces annual electrical usage by 33%, compared with continuous operation mode. Hence, the model can assist in optimizing a given system's operation to get the most value out of a geothermal network installation. Future work will consider the model's application to a demonstration project, including the model validation against operational data and system operation optimization.

ambient-temperature loop↗
DOE OSTI2024

AGN-201 Digital Twin Concept of Operations

The AGN-201 is a nuclear reactor at Idaho State University (ISU) and is currently being used in the development of a digital twin (DT) with Idaho National Laboratory (INL). The goal is to create a DT (called the AGN-201 DT) which can monitor an operating nuclear reactor to determine when the reactor is being operated normally; normal operations are any operations that is declared by the ISU staff. To accomplish this, researchers at INL developed a DT ecosystem which can ingest data from the AGN-201 reactor. This data is fed into a series of machine learning (ML) and reactor physics models. The ML and reactor physics models assess the data and determine if the reactor is operating normally. Event(s) that are flagged as anomalies are investigated by the INL staff to determine if any undeclared experiments were conducted. The AGN-201 DT was verified in July/August 2023, when the ISU staff performed undeclared experiments and the INL staff were able to assess the anomalous events and determine what likely caused each event. This project was a steppingstone to promote the use of DTs for international safeguards and marks the first time a DT was able to monitor a nuclear reactor for this purpose. This document provides the concept of operations (CONOPS) for the AGN-201 digital twin (DT). The CONOPS describes how the AGN-201 and AGN-201 DT are expected to operate and details how each are established.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

A Theoretical Operational Model for Complex Experiments and its Invariance Theorems

We develop and systematize the Theoretical–Operational Model (TOM), a framework that treats preparation and measurement —including their operational residues— as intrinsic structures of physical theory. The central contribution is a principled geometric–algebraic organization of admissible operational deformations, formulated using quantum channels, renormalization-style flows, and information-geometric tools. Within this structure, operational residues and background processes are represented as effective morphisms attached to these operational components, whose invariants yield constraints on how theoretical parameters vary under specified classes of deformations. Illustrations drawn from muon–electron conversion, long-baseline neutrino oscillations, and quark–gluon-plasma phenomenology show how TOM maps operational effects into inferences about theoretical parameters, enables systematic cross-experimental comparisons, and stabilizes parameter estimation against defined deformation families. By embedding the operational layer—together with its residues—within a structured theoretical setting, TOM supports both theory testing and theory development, clarifying the conceptual relation between experimental realization and the physical quantities represented by the theory.

Pronskikh, Vitaly [Fermilab] (ORCID:00000002518174↗
DOE OSTI2023

Solar Uncertainty Management and Mitigation for Exceptional Reliability in Grid Operations (SUMMER-GO): Project Final Report

The Solar Uncertainty Management and Mitigation for Exceptional Reliability in Grid Operation (SUMMER-GO) project was recently completed through a collaboration among the National Renewable Energy Laboratory, Maxar, the Electric Reliability Council of Texas (ERCOT), the University of Texas at Dallas, the University of California Berkeley, and the University of Colorado Boulder. The project made significant advances in probabilistic solar power forecasting, both through the development of Bayesian model averaging methods for ensemble forecasting and in bringing these and other advancements into practice with Maxar's delivery of operational forecasts to ERCOT. In addition to creating more reliable solar power forecasts, the project developed methods for their utilization in power system operations. These include the development of risk-aware unit commitment and economic dispatch algorithms and methods to reformulate probabilistic forecasts to be used in these power system operational models. Dynamic power system reserve methods were also developed, which have been shown in silico to create economic savings and reliability improvements on an ERCOT-like system as well as financial savings in the ERCOT system through more granular consideration of the uncertainty associated with solar power forecasts. Finally, a situational awareness tool to help grid operators better understand solar power forecast uncertainty in daily operations was developed and extensively vetted.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Streamlining Tactical Operator Handoffs During Multi-Vehicle Applications

Increased automation has shifted the operator control paradigm from a single operator controlling a single vehicle, to multiple operators collaborating to control multiple vehicles; this paradigm is known as m:N. Many questions remain unanswered in this new operational paradigm about the division of assets as workload for individual operators varies overtime. This paper explores the management of workload by enabling operators to temporarily handoff vehicles among each other. A study was conducted to explore both a manual and assisted method for performing handoffs during manipulated contingency scenarios. The assisted handoff method allowed subjects to easily choose and group nominal and/or contingency vehicles. The number of contingencies was also manipulated to determine the effect workload had on how pilots utilized the ability to handoff vehicles. Results show subjects performed handoffs more often when there were more contingencies and when the assisted handoff tool was available. In addition, the assisted tool make subjects feel more comfortable, enabling them to feel like they could take longer to resolve contingency situations. Lastly, even during contingencies, subjects were able to successfully complete secondary tasks.

M:N operations↗
DOE OSTI2017

Reactor operator-in-the-loop dynamic modelling - 122

The description of human operator dynamic characteristics in mathematical terms compatible with control engineering practice is an essential prerequisite to the analytical treatment of manual reactor control systems. Safe reactor operation requires effective operator control through interaction with plant dynamics, manipulators and displays. Traditional static analysis methods consider only specific situations; they fail to adequately explain the mutual interactions between the operator and the reactor plant characteristics. In this paper we investigate the theory for describing operator-reactor characteristics based on the methods of conventional control engineering techniques. The primary purpose of the experiments reported is the validation of the quasi-linear operator model. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Feasibility study on ultralong-cycle operation and material performance for compact liquid metal-cooled fast reactors: a review work

This paper reviews the feasibility of ultralong-cycle operation on a compact liquid metal-cooled fast reactor (LMR) firstly by assessing the operation of a long-life fast reactor core and secondly by evaluating material performance in respect to both long-cycle operation and compact-size fast reactor. Many kinds of reactor concepts have been proposed, and LMR and small modular reactor (SMR) are the issued leading technologies for generation four (Gen-IV) reactor system development. The breed-and-burn strategy was proposed as a core burning strategy to operate a long cycle, and it has been evaluated in this paper with two reactor concepts: constant axial shape of neutron flux, nuclide densities, and power shape during life of energy and ultralong cycle fast reactor. In addition, Super-Safe, Small, and Simple and small modular fast reactor, compact LMR concepts, have been simulated to evaluate their long-life operation strategies. For the other practical issues, the materials for fuel, coolant, and structure have been identified and some of them are selected to have their performance optimized specifically for compact LMR with a long-cycle operation. It is believed that this comprehensive review will propose a proper direction for future reactor development and will be followed by the next step research for a complete reactor model with the other reactor components.

breed-and-burn↗
DOE OSTI2021

A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system

Hydro-wind-photovoltaic (PV) hybrid system has the potential to increase the integration of renewable energy sources into an existing grid. For the long-term operation of the system, due to the non-storable nature of wind and PV power, it is essentially to decide the optimal long-term carryover storage of cascade reservoirs. However, it remains a challenge due to high uncertainties of long-term forecasts and complicated hydraulic/electrical relationships between cascade reservoirs. Here in this study, a forecast-driven decision-making model is proposed for the hybrid system, which converts the multi-stage long-term operation process into a two-stage operation problem (including current stage and carryover stage) to avoid using longer-horizon forecast information with lower accuracy. First, the carryover stage energy surfaces (CESs) considering the forecast uncertainties of wind, solar and hydro resources are proposed to characterize carryover stage benefit quantitatively. Then a CESs-based forward decision-making optimization model is developed to guide the long-term operation of a hydro-wind-photovoltaic hybrid system. The applications in a hydro-wind-PV hybrid system of Yalong River basin results show that: compared with conventional operation, 1) power generation increases 9.03%; 2) in terms of the carryover storages control, the reservoir impounding and drawdown timing are delayed, and the drawdown depth is increased, which can be used to formulate better reservoir operation rules.

13 HYDRO ENERGY↗
DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗
DOE OSTI2025

A higher-order finite-element implementation of the nonlinear Fokker–Planck collision operator for charged particle collisions in a low density plasma

Collisions between particles in a low density plasma are described by the Fokker–Planck collision operator. In applications, this nonlinear integro-differential operator is often approximated by linearised or ad-hoc model operators due to computational cost and complexity. In this work, we present an implementation of the nonlinear Fokker–Planck collision operator written in terms of Rosenbluth potentials in the Rosenbluth–MacDonald–Judd (RMJ) form. The Rosenbluth potentials may be obtained either by direct integration or by solving partial differential equations (PDEs) similar to Poisson's equation: we optimise for performance and scalability by using sparse matrices to solve the relevant PDEs. We represent the distribution function using a tensor-product continuous-Galerkin finite-element representation and we derive and describe the implementation of the weak form of the collision operator. We present tests demonstrating a successful implementation using an explicit time integrator and we comment on the speed and accuracy of the operator. Finally, we speculate on the potential for applications in the current and next generation of kinetic plasma models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Strategies for microgrid operation under real-world conditions

Microgrids are an increasingly relevant technology for integrating renewable energy sources into electricity systems. Based on a microgrid implementation in California, in this study we investigate microgrid operation under real-world conditions. These conditions have not yet been considered in combination and encompass energy charges, demand charges, export limits, as well as uncertainty about future electricity demand and generation in the microgrid. Under these conditions, we evaluate the performance of two frequently applied groups of strategies for microgrid operation. The first group is composed of proactive strategies that optimize decisions based on forecasts of future electricity generation and demand. The second group includes reactive strategies that make operational decisions based exclusively on the current state of the microgrid. We evaluate the performance of the strategies under varying operational parameters, forecast accuracies, and microgrid configurations—well beyond our Californian showcase. Our results confirm the expectation that proactive strategies outperform reactive ones in the majority of settings. Yet, reactive strategies can perform better under short control intervals or under moderate prediction errors of PV generation or demand. Furthermore, the interplay between real-world conditions and operational strategies reveals several additional insights for research on microgrid operation. First, we find that demand charges and export limits decisively affect microgrid performance. Second, the impact of forecast errors is highly non-linear and non-monotonous. Third, escalating negative interactions between forecast errors and demand charges make proactive strategies benefit from longer control intervals. This result is contrary to existing best practice, which promotes short control intervals to minimize the impact of uncertainty.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Co-optimization of nuclear reactor flexible power operation and maintenance scheduling

As flexible power operation of nuclear power plants becomes more attractive due to the reduction in fossil-fueled dispatchable generation on energy grids, finding optimal power production strategies that balance revenue generation with operational concerns becomes more complex. This article presents a general framework to aid operators in designing economically optimal long term dispatch strategies for nuclear power plants. The principal novelty is the linking of estimated system remaining useable life (RUL) to strategic operational decisions. It is shown that, depending on the relationship between the fixed costs from maintenance and the associated lost revenue from an outage, it can be economically optimal in the long term to delay a maintenance outage and not perform this alongside refueling. For a given relationship between power ramping and degradation, optimal strategies were found that discouraged load following in some situations while minimizing unnecessary maintenance. It is shown that heavy load following can cause maintenance and refueling outages to diverge due to their inverse relationships with respect to load following, potentially leading to a significant loss in capacity factor. As a result, this general framework can be applied to specific reactor dispatch allowing operators to adapt operational strategies as future grid conditions change.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Neural-network based collision operators for the Boltzmann equation

Kinetic gas dynamics in rarefied and moderate-density regimes have complex behavior associated with collisional processes. These processes are generally defined by convolution integrals over a high-dimensional space (as in the Boltzmann operator), or require evaluating complex auxiliary variables (as in Rosenbluth potentials in Fokker-Planck operators) that are challenging to implement and computationally expensive to evaluate. In this work, we develop a data-driven neural network model that augments a simple and inexpensive BGK collision operator with a machine-learned correction term, which improves the fidelity of the simple operator with a small overhead to overall runtime. The composite collision operator has a tunable fidelity and, in this work, is trained using and tested against a direct-simulation Monte-Carlo (DSMC) collision operator.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Active operator learning with predictive uncertainty quantification for partial differential equations

With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions and the associated error levels is necessary for deploying reliable surrogate models in scientific applications. Existing uncertainty quantification (UQ) frameworks employ ensembles or Bayesian methods, which can incur substantial computational costs during both training and inference. Here, we propose a lightweight predictive UQ method tailored for Deep operator networks (DeepONets) that also generalizes to other operator networks. Numerical experiments on linear and nonlinear PDEs demonstrate that the framework’s uncertainty estimates are unbiased and provide accurate out-of-distribution uncertainty predictions with a sufficiently large training dataset. Our framework provides fast inference and uncertainty estimates that can efficiently drive outer-loop analyses that would be prohibitively expensive with conventional solvers. We demonstrate how predictive uncertainties can be used in the context of Bayesian optimization and active learning problems to yield improvements in accuracy and data-efficiency for outer-loop optimization procedures. In the active learning setup, we extend the framework to Fourier Neural Operators (FNO) and describe a generalized method for other operator networks. To enable real-time deployment, we introduce an inference strategy based on precomputed trunk outputs and a sparse placement matrix, reducing evaluation time by more than a factor of five. Our method provides a practical route to uncertainty-aware operator learning in time-sensitive settings.

97 MATHEMATICS AND COMPUTING↗