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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Bottom-Up Cost Estimation Tool for Nuclear Microreactors

The rising interest in nuclear microreactors has highlighted the need for comprehensive technoeconomic assessments. However, the scarcity of publicly available designs and cost data has posed significant challenges. To address this issue, the Microreactor Optimization Using Simulation and Economics (MOUSE) tool is developed. MOUSE is a tool that integrates nuclear microreactor design with reactor economics. The design calculations encompass core simulations using the OpenMC Monte Carlo Particle Transport Code [romano2015], along with simplified balance of plant calculations. On the economic side, MOUSE provides detailed bottom-up cost estimates, calculating both the total capital cost and the levelized cost of energy for first-of-a-kind and nth-of-a-kind microreactors. The cost estimation correlations are developed using data from the MARVEL project and additional literature sources. MOUSE has released as an open-source tool on GitHub (MOUSE Tool). By combining design calculations with cost equations, MOUSE enables users to evaluate the impact of various technological consideration, advanced moderators, design changes, material/fuel changes, and geometry modifications—as well as economic parameters like interest rates and construction duration. This comprehensive framework can guide stakeholders towards technological solutions that enhance microreactor competitiveness. Additionally, powered by the WATTS toolkit [romano2022], MOUSE supports optimization studies, parametric analyses, and uncertainty calculations/propagation. Currently, preconceptual designs of three microreactor types are included in MOUSE: a liquid metal thermal microreactor (LTMR), gas cooled TRISO-fueled microreactor (GCMR) and heat-pipe TRISO fueled microreactor (HPMR). To showcase its ability, MOUSE was used to conduct detailed bottom-up cost estimates for the first of a kind (FOAK) and Nth of a kind (NOAK) of the following microreactors • A 20MWt LTMR that is built on the ongoing MARVEL demonstration at Idaho National Laboratory (INL) • A 15 MWt GCMR that was designed to be more representative of the typical commercial microreactor • A 7 MWt HPMR that was built on previous work (Choi 2024) The The reader should note that these three designs and corresponding cost estimates are examples to demonstrate the MOUSE capability. The designs are pre-conceptual, the reactor designs were not optimized, and the cost estimates were developed with incomplete information. Additionally, stakeholders might be interested in a variety of designs that may differ from the examples provided in this report. The MOUSE tool can also be used to study how design choices affect economics. To demonstrate its capability, MOUSE was used conduct parametric studies such as examining the economic impact of the reflector's material and thickness, the moderator's booster material and dimensions, fuel composition and enrichment, core size, and power level. Several insights were gained from these parametric studies.

Hanna, Botros↗
DOE OSTI2023

Enabling Hyper-Differential Sensitivity Analysis for Ill-Posed Inverse Problems

Inverse problems constrained by partial differential equations (PDEs) play a critical role in model development and calibration. In many applications, there are multiple uncertain parameters in a model that must be estimated. However, high dimensionality of the parameters and computational complexity of the PDE solves make such problems challenging. A common approach is to reduce the dimension by fixing some parameters (which we will call auxiliary parameters) to a best estimate and use techniques from PDE-constrained optimization to estimate the other parameters. In this article, hyper-differential sensitivity analysis (HDSA) is used to assess the sensitivity of the solution of the PDE-constrained optimization problem to changes in the auxiliary parameters. Foundational assumptions for HDSA require satisfaction of the optimality conditions which are not always practically feasible as a result of ill-posedness in the inverse problem. Here we introduce novel theoretical and computational approaches to justify and enable HDSA for ill-posed inverse problems by projecting the sensitivities on likelihood informed subspaces and defining a posteriori updates. Our proposed framework is demonstrated on a nonlinear multiphysics inverse problem motivated by estimation of spatially heterogeneous material properties in the presence of spatially distributed parametric modeling uncertainties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Error Estimates of Residual Minimization Using Neural Networks for Linear PDES

We propose an abstract framework for analyzing the convergence of least-squares methods based on residual minimization when feasible solutions are neural networks. With the norm relations and compactness arguments, we derive error estimates for both continuous and discrete formulations of residual minimization in strong and weak forms. The formulations cover recently developed physicsinformed neural networks based on strong and variational formulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Gaussian Process Optimization of Sensitivity-Based Similarity Metrics between New Nuclear Applications and New/Existing Benchmarks [Slides]

This presentation discusses Nuclear Criticality Safety (NCS) and how designing safe, new nuclear criticality experiments requires expert judgement, which could take years of experience. Sensitivity/uncertainty (S/U) analysis can be utilized by less experienced individuals to conservatively estimate uncertainties in important parameters, such as k eff , in newly proposed nuclear experiments. The presentation poses the question of how this analysis can be performed and states that the answer lies in matching new nuclear experiments with existing benchmark experiments using similarity metrics. By increasing the criticality safety of the application in this work, higher mass limits could be used in PF-4 operations. Additionally, the presentation discusses MCNP6.2®, Whisper-1.1, the software that can be used in this analysis. Also discussed is the fact that International Criticality Safety Benchmark Evaluation Project (ICSBEP) benchmarks rarely match new nuclear applications and that there are significant differences in given set of materials and/or geometry. If there are no benchmarks that match the application, the presentation discusses the possibility of creating new benchmarks. In conclusion, this work presents a Gaussian process (GP) optimization scheme that was used to generate new benchmarks with the highest sensitivity-based similarity metrics to user-defined nuclear applications. The Gaussian process optimization successfully designed 3 new experimental benchmarks that were highly correlated to the application of interest and had k eff values near critical. Optimization over c k,i-r has shown that investigating specific isotope reactions for different applications is crucial to designing benchmark experiments. Partial contribution from Pu dominates c k similarity metric. Future work includes testing new stand-alone similarity metrics or new combinations of similarity metrics as the design criterion of this optimization – design criterion is application dependent.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1991

Inverse problems and optimal experiment design in unsteady heat transfer processes identification

Experimental-computational methods for estimating characteristics of unsteady heat transfer processes are analyzed. The methods are based on the principles of distributed parameter system identification. The theoretical basis of such methods is the numerical solution of nonlinear ill-posed inverse heat transfer problems and optimal experiment design problems. Numerical techniques for solving problems are briefly reviewed. The results of the practical application of identification methods are demonstrated when estimating effective thermophysical characteristics of composite materials and thermal contact resistance in two-layer systems.

Artyukhin, Eugene A.↗
DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhancing Smart Home Privacy: A Tutorial on Local Differential Privacy Techniques for Frequency and Mean Estimation

The ubiquity of Internet of Things (IoT) systems has seamlessly integrated into our daily lives, particularly in smart homes where devices continuously monitor and optimize our living environments. These systems significantly contribute to home automation, energy efficiency, and overall comfort. However, this widespread connectivity poses inherent risks linked to the streaming of sensitive household data, necessitating robust privacy preservation mechanisms. This tutorial systematically examines privacy preservation through local differential privacy (LDP), with a particular focus on frequency and mean estimation techniques for smart home applications. Here, we present a comprehensive taxonomy of smart home data formats and provide detailed implementation guidance for event-based and w-event LDP mechanisms. Through practical examples using smart thermostats and HVAC systems, we demonstrate how these techniques can be effectively deployed in real-world scenarios. The tutorial concludes by examining emerging research directions, including adaptive privacy budgets and federated learning approaches, establishing a foundation for privacy-preserving smart home deployments.

Kotevska, Olivera [Oak Ridge National Laboratory (↗
DOE OSTI2023

A General Spatiotemporal Imputation Framework for Missing Sensor Data

Many applications from precision agriculture, environmental monitoring and transportation networks rely on data collected across space and time over a large geographic area. Missing data poses a significant challenge for any data-driven inference and control tasks. Data imputation or the estimation of missing data can help fill these gaps by utilizing inherent spatial relationships and temporal patterns. A variety of spatiotemporal imputation models have been developed to address missing data in spatiotemporal datasets. However, these classical methods rely on the assumption that the underlying data follows a smooth trend and fail to provide accurate estimates when there is a large number of missing points in the data. Even though there are machine learning driven tensor completion approaches such as convolutional neural network based tensor completion (CoSTCo) that capture the non-linear relationships in the dataset, the transductive nature makes the algorithm less scalable. Thus, existing approaches for estimating the missing information do not effectively capture all dimensions of the spatiotemporal data structure, resulting in erroneous predictions and poor performance. The main contributions of this paper are: (1) We propose a novel inductive framework (G-LSTM) for missing data imputation that integrates a graph neural network with LSTMs to effectively capture both spatial and temporal dependencies. (2) Experimental results on a traffic dataset demonstrate that the proposed GNN integrated with an LSTM framework achieves improved imputation and maintains steady performance even when there are extreme missing conditions in comparison with the state-of-the-art imputation framework (i.e, CoSTCo). (3) The simulation results on a traffic network show up to 69% reduction in mean absolute error and 61% reduction in root mean square error when compared to CoSTCo.

Tharzeen, Aabila↗
DOE OSTI2022

Bayesian Inference of State-Level COVID-19 Basic Reproduction Numbers across the United States

Although many persons in the United States have acquired immunity to COVID-19, either through vaccination or infection with SARS-CoV-2, COVID-19 will pose an ongoing threat to non-immune persons so long as disease transmission continues. We can estimate when sustained disease transmission will end in a population by calculating the population-specific basic reproduction number ℛ0, the expected number of secondary cases generated by an infected person in the absence of any interventions. The value of ℛ0 relates to a herd immunity threshold (HIT), which is given by 1−1/ℛ0. When the immune fraction of a population exceeds this threshold, sustained disease transmission becomes exponentially unlikely (barring mutations allowing SARS-CoV-2 to escape immunity). Here, we report state-level ℛ0 estimates obtained using Bayesian inference. Maximum a posteriori estimates range from 7.1 for New Jersey to 2.3 for Wyoming, indicating that disease transmission varies considerably across states and that reaching herd immunity will be more difficult in some states than others. ℛ0 estimates were obtained from compartmental models via the next-generation matrix approach after each model was parameterized using regional daily confirmed case reports of COVID-19 from 21 January 2020 to 21 June 2020. Our ℛ0 estimates characterize the infectiousness of ancestral strains, but they can be used to determine HITs for a distinct, currently dominant circulating strain, such as SARS-CoV-2 variant Delta (lineage B.1.617.2), if the relative infectiousness of the strain can be ascertained. On the basis of Delta-adjusted HITs, vaccination data, and seroprevalence survey data, we found that no state had achieved herd immunity as of 20 September 2021.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2012

Characterization of New Hard X-ray Cataclysmic Variables

Aims. We aim at characterizing a sample of nine new hard X-ray selected Cataclysmic Variable (CVs), to unambiguously identify them as magnetic systems of the Intermediate Polar (IP) type. Methods. We performed detailed timing and spectral analysis by using X-ray, and simultaneous UV and optical data collected by XMM−Newton, complemented with hard X-ray data provided by INTEGRAL and Swift. The pulse arrival time were used to estimate the orbital periods. The broad band X-ray spectra were fitted using composite models consisting of different absorbing columns and emission components. Results. Strong X-ray pulses at the White Dwarf (WD) spin period are detected and found to decrease with energy. Most sources are spin-dominated systems in the X-rays, though four are beat dominated at optical wavelengths. We estimated the orbital period in all system (except for IGR J16500-3307), providing the first estimate for IGRJ08390-4833, IGRJ18308-1232, and IGR J18173-2509. All X-ray spectra are multi-temperature. V2069 Cyg and RX J0636+3535 poses a soft X-ray optically thick component at kT approx. 80 eV. An intense K (sub alpha) Fe line at 6.4 keV is detected in all sources. An absorption edge at 0.76 keV from OVII is detected in IGR J08390-4833. The WD masses and lower limits to the accretion rates are also estimated. Conclusions. We found all sources to be IPs. IGR J08390-4833, V2069 Cyg, and IGR J16500-3307 are pure disc accretors, while IGR J18308-1232, IGR J1509-6649, IGR J17195-4100, and RX J0636+3535 display a disc-overflow accretion mode. All sources show a temperature gradient in the post-shock regions and a highly absorbed emission from material located in the pre-shock flow which is also responsible for the X-ray pulsations. Reflection at the WD surface is likely the origin of the fluorescent iron line. There is an increasing evidence for the presence of a warm absorber in IPs, a feature that needs future exploration. The addition of two systems to the subgroup of soft X-ray IPs confirms a relatively large (∼ 30%) incidence.

hard X-ray↗
NASA NTRSDate not supplied

Lunar Regolith Trajectories as a Result of Plume Surface Interactions

Lunar regolith is ejected from the impingement points of descent engine plumes. Such particles pose potential risks to surface operations, sites of scientific and historical interest, and orbiting spacecraft. Consequently, determining the resultant trajectories of these particles is necessary in order to estimate and mitigate risk. Here we present the ranges, impact latitudes, times of flight, and maximum altitudes for particles accelerated by a plume surface interaction at the lunar south pole. Using launch angles determined from observations and simulations, and for velocities <1:6 km/s, particles pose little risk. However, above 1:6 km/s the risks increase, and the results become highly sensitive to the initial angle. In addition, gravitational and non-gravitational processes will introduce perturbations to high-velocity trajectories resulting in a reduction in precision. Therefore, while local topography or artificial berms may mitigate trajectories with low initial angles, it remains important to place tight constraints on the potential launch angles of particles accelerated by plume surface interactions through simulations and experimentation. If these angles are indeed constrained to within a few degrees of the horizon, the risks posed by accelerated regolith particles at any velocity will be minimal.

Daniel Batcheldor↗
NASA NTRS2021

Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments

In recent years, unsupervised deep learning ap-proaches have received a significant attention to estimate depthand visual odometry (VO) from unlabelled monocular imagesequences. However, their performance is limited in challengingenvironments due to perceptual degradation, occlusions andrapid motions. Moreover, the existing unsupervised methodssuffer from the lack of scale-consistency constraints acrossframes, which causes that the VO estimators fail to providepersistent trajectories over long sequences. In this study, wepropose a unsupervised monocular deep VO framework thatpredicts 6 degrees-of-freedom pose camera motion and depthmap of the scene from unlabelled RGB image sequences.We provide detailed quantitative and qualitative evaluationsof the proposed framework on a) a challenging dataset col-lected during the DARPA Subterranean challenge1; and b)the benchmark KITTI and Cityscapes datasets. The proposedapproach outperforms both traditional and state-of-the-artunsupervised deep VO methods providing better results for bothpose estimation and depth recovery. The presented approach ispart of the solution used by the COSTAR team participatingat the DARPA Subterranean Challenge

Agha-mohammadi, Ali-akbar↗
DOE OSTI2024

A Time-Domain Protection Approach for AC Transmission Systems With Grid-Forming Resources

Ac transmission protection must reliably detect, classify, and locate short-circuit faults from voltage and current measurements. At present, these functionalities, which have been classically engineered using phasors approaches, are being challenged by the dynamic behavior and fault-current limits of converter-based generation. This paper tackles these challenges by engineering a time-domain protection approach that leverages the classical Bergeron model in a new manner. Low- and high-impedance faults are detected and classified by ascertaining how well line voltage and current measurements match the Bergeron equations. Faults are located by posing a novel one-variable optimization problem, whereas voltage and current waveforms at the fault location are estimated by unveiling rigorous relationships. The proposed elements are secure against external faults, measurement errors, and variation of line parameters and sampling time. Furthermore, these advances are tested via electromagnetic transient simulations and are significant to satisfy IEEE and North American Electric Reliability Corporation requirements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

MCBI in an Electron-Ion Collider

The luminosity performance for an electron-ion collider demands high-current operation for both the electron and ion beams, for a wide range of collision energies. This poses many challenges on the beam stability with regard to collective effects. In this paper, we present preliminary estimations of coherent instabilities for both of the EIC designs, i.e., JLEIC and eRHIC. Mitigation mechanisms or schemes envisioned for suppression of the instabilities are also discussed.

Li, Rui↗
NASA NTRS2010

New Evidence for a Black Hole in the Compact Binary Cygnus X-3

The bright and highly variable X-ray and radio source known as Cygnus X-3 was among the first X-ray sources discovered, yet it remains in many ways an enigma. Its known to consist of a massive. Wolf-Rayet primary in an extremely tight orbit with a compact object. Yet one of the most basic of pa.ranietern the mass of the compact object - is not known. Nor is it even clear whether its is a neutron star or a black hole. In this Paper we present our analysis of the broad-band high-energy continua covering a substantial range in luminosity and spectral morphology. We apply these results to a recently identified scaling relationship which has been demonstrated to provide reliable estimates of the compact object mass in a number of accretion powered binaries. This analysis leads us to conclude that the compact object in Cygnus X-3 has a mass greater than 4.2 solar mass thus clearly indicative of a black hole and as such resolving a longstanding issue. The full range of uncertainty in our analysis and from using a. range of recently published distance estimates constrains the compact object mass to lie between 4.2 solar mass and 14.4 solar mass. Our favored estimate, based on a 9.0 kpc distance estimate is approx. l0 solar mass, with the. error margin of 3.2 solar masses. This result may thus pose challenges to shared-envelope evolutionary models of compact binaries. as well as establishing Cygnus X-3 as the first confirmed accretion-powered galactic gamma: ray source.

Shrader, Chris R.↗
DOE OSTI2024

Activation of Insulation in Low Power Nuclear Experiments

Nuclear experiments aim to test new designs, procedures, and materials. Similar to conventional power reactors, potential sources of worker exposure to radiation is a primary concern. One such concern is the insulation materials that are used in or near the reactor, which can often create dust when being handled. If this material receives a substantial neutron dose, then it can become activated, which can pose a health risk if inhaled or ingested. This analysis looked at the activation of, and conservative dose estimates from, industrial grade insulations that may be used in a reactor experiment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2000

Aerosol Climate Effects: Local Radiative Forcing and Column Closure Experiments

In an effort to reduce uncertainties in climate change predictions, experiments are being planned and conducted to measure anthropogenic aerosol properties and effects, including effects on radiative fields. The global average, direct anthropogenic aerosol effect on upwelling shortwave fluxes is estimated to be about +1/2 W/sq m, whereas errors in flux changes measured with airborne and spaceborne radiometers are 2 to 8 W/sq m or larger. This poses the question of whether flux changes expected in field experiments will be large enough to measure accurately. This paper obtains a new expression for the aerosol-induced change in upwelling flux, compares it to two-stream and adding-doubling (AD) results, and uses all three methods to estimate expected flux changes. The new expression accounts for the solar zenith angle dependences of aerosol transmission and reflection, as well as of surface albedo, all of which can have a strong effect in determining flux changes measured in field experiments. Despite its relative simplicity, the new expression gives results similar to previous two-stream results. Relative to AD results, it agrees within a few watts per square meter for the intermediate solar elevation angles where the flux changes peak (roughly 10 to 30 degrees), but it has negative errors for higher Sun and positive errors for lower Sun. All three techniques yield aerosol-induced changes in upwelling flux of +8 to +50 W/sq m for aerosol midvisible optical depths of 0.1 to 0.5. Because such aerosol optical depths occur frequently off the U.S. and European Atlantic coasts in summer, the flux changes they induce should be measurable by airborne, and possibly by spaceborne, radiometers, provided sufficient care is taken in experiment design (including measurements to separate aerosol radiative effects from those of absorbing gases). The expected flux changes are about 15 to 100 times larger than the global average flux change expected for the global average anthropogenic sulfate optical depth of 0.04. Reasons for the larger flux changes include the larger optical depths considered here (factor 2.5 to 12), plus restricting the measurements to cloud-free, daytime conditions over the ocean (factor 5 to 9).

Russell, P. B.↗
DOE OSTI2024

Quantifying the economic costs of power outages owing to extreme events: A systematic review

Quantifying the economic cost of long-duration power outages is crucial to justifying investments in resiliency and reliability improvements. However, extensive study on the subject complicates the identification of power outage costs and determining the most suitable approach to quantify them for an individual, specific facility, particularly in the context of extreme events. Here, this research provides a systematic review of economic studies estimating the impact of environmental disasters at the microeconomic and macroeconomic levels. Of 326 articles, evaluating the costs of power outages in extreme events, this work identified 22 studies that attempted to quantify the economic costs. These findings indicate that quantifying power outage costs lacks standardization, posing challenges for comparing different studies. Most analyses aiming to quantify these costs for utilities, sectors, and the overall economy rely on outdated survey data, which offer generalized rather than specific cost estimations. The costs of power outages exhibit a significant dependence on factors such as the sector involved, the type of customer affected, and the outage duration. To quantify industry costs, the research in this study suggests that using the National Renewable Energy Laboratory's online, open-access Customer Damage Function Calculator is the best option for individual-level assessments of industries, hospitals, offices, education centers, and similar facilities. However, the Interruption Cost Estimate Calculator can estimate outage costs across industrial, commercial, and residential sectors for macroeconomic outcomes. Finally, this article discusses the relative strengths of these methods and tools and the potential directions for future research.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗