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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Estimation of Fission Product Transport Parameters for Cesium in the AGR-3/4 TRISO Fuel Experiment

A one-dimensional (1D) finite-element model of fission product transport in the AGR-3/4 experiment has been developed using the Multiphysics Object Oriented Simulation Environment (MOOSE) framework and implemented in the fuel performance code, BISON. The model resolves capsule-specific geometries, materials, and temperature histories and simulates radial migration of fission products from the fuel compact through the inner ring, outer ring, and into the sink ring. Model parameters governing diffusion and sorption were estimated for key fission products – cesium (Cs), and europium (Eu) – by simultaneously fitting modeled isotopic concentration profiles and total ring inventories to a post-irradiation experimental measurement. These data include gamma scanning, liquid scintillation for Sr-90, radial deconsolidation leach-burn-leach analysis, tomographic reconstructions, and destructive physical sampling. A mortar-based interfacial sorption framework was implemented to enforce physically consistent mass transfer and flux conservation across gas gaps. Two classes of parameter sets were derived: a least-squares best-fit, and a safety-oriented conservative-fit, what applies strong penalties for underprediction of sink inventories. Across all twelve capsules, the model successfully reproduces the dominant radial transport trends for Cs, Sr, with decreasing concentrations from the compact outward through successive rings. Cs behavior is captured most consistently, while strontium predictions reveal systematic trade-offs between compact accuracy and conservative sink-ring bounding. The results demonstrate that sink ring weighted calibration provides conservative, safety relevant bounds on low temperature fission product transport, but at the cost of underpredicting compact inventories for Sr isotopes. These discrepancies highlight the need for additional physics, including fast-slow diffusion model, incorporating trapping mechanism in the transport behavior. Overall, this work establishes a robust, capsule-specific modeling framework for AGR-3/4 fission product transport and provides a defensible basis for parameter selection in source-term and fuel performance analyses for high temperature gas-cooled reactors.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Data-Driven Recommendation of Optimal Tuning Scheme for Range-Separated Hybrid Functionals in Solution-Phase UV/Vis Absorption Energy Prediction

Time-dependent density functional theory (TDDFT) combined with range-separated hybrid (RSH) functionals and a tuned range-separation parameter γ offers a computationally economical approach for high-throughput excited- state property predictions. The γ-tuning procedure in the gas phase is well established. However, no agreement on the best γ- tuning procedure has been made when considering the solvent effect with implicit solvent models like the polarizable continuum model (PCM). To answer that question, this study created a diverse dataset with 937 molecules with experimental solutionphase UV/vis absorption spectra. Three γ-tuning methods, the gasphase γ-tuning (GPγT), the partial vertical γ-tuning (PVγT), and the strict vertical γ-tuning (SVγT), were evaluated for the ωPBEh functional over the entire dataset. Additional benchmarks are done for the optimally tuned screened range-separated hybrid combined with the PCM approach (SRSH-PCM) and the solvation-mediated tuning procedure (sol-med-OT). Our findings revealed that the optimal γ-values obtained by the PVγT and the SVγT are significantly smaller than the GPγT. This trend holds consistently across all molecules in our dataset, and we explained the origin of this phenomenon. TDDFT calculations with PVγTand SVγT-tuned γ-values and default global Fock exchange fraction achieve superior performance compared to those using GPγTtuned or default γ and slightly outperform SRSH-PCM and sol-med-OT with similar or lesser computational cost. Furthermore, we found that the smaller γ-values from SVγT captured the expected 1/(εR) asymptotic behavior in the solution phase, resulting in accurate prediction of solution-phase CT excitations, consistent with the screened asymptote behavior encoded in SRSH-PCM. These results show that SVγT is the best scheme for high-throughput UV/vis absorption spectrum calculations using the ωPBEh functional from a data-driven perspective.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Capturing Surface Coverage Effects in Heterogeneous Catalysis

Adsorbate–adsorbate lateral interactions at relevant surface coverages have a significant effect on chemical kinetics, thereby influencing the activity of a heterogeneous catalyst. Coverage-dependent kinetic and thermodynamic parameters therefore must be included in studies of such complex systems to properly predict the turnover frequencies and kinetic trends. Thus, it becomes extremely important to accurately capture the strength of lateral interactions between neighboring species under realistic reaction conditions. In this Perspective, we discuss the various existing computational and experimental methods for determining adspecies coverage and configurational effects. The choice of the tools and methods employed in such studies depends on factors such as time, length scales, computational cost, the presence of solvents, and reaction conditions. The applications of each method and the respective challenges are also discussed here. As a result, we discuss the recent developments and future of the state-of-the-art for inclusion of surface coverage and configuration into a holistic picture for accurate predictions of catalytic behavior.

09 BIOMASS FUELS↗
DOE OSTI2022

The Mira–Titan Universe – IV. High-precision power spectrum emulation

Modern cosmological surveys are delivering data sets characterized by unprecedented quality and statistical completeness; this trend is expected to continue in the future as new ground- and space-based surveys come online. In order to maximally extract cosmological information from these observations, matching theoretical predictions are needed. At low redshifts, the surveys probe the non-linear regime of structure formation where cosmological simulations are the primary means of obtaining the required information. The computational cost of sufficiently resolved large-volume simulations makes it prohibitive to run very large ensembles. Nevertheless, precision emulators built on a tractable number of high-quality simulations can be used to build very fast prediction schemes to enable a variety of cosmological inference studies. We have recently introduced the Mira–Titan Universe simulation suite designed to construct emulators for a range of cosmological probes. This gravity-only set of simulations covers the standard six cosmological parameters {ω m , ω b , σ 8 , $h, n_s, w_0$} and, in addition, includes massive neutrinos and a dynamical dark energy equation of state {ω ν , $w_a$}. In this paper, we present the final emulator for the matter power spectrum based on 111 cosmological simulations, each covering a (2.1 Gpc) 3 volume and evolving 3200 3 particles. In this work, an additional set of 1776 lower resolution simulations and TimeRG perturbation theory results for the power spectrum are used to cover scales straddling the linear to mildly non-linear regimes (maximum wavenumber $\textit{k}$ = 5 Mpc –1 ). The emulator provides predictions at the 2–3 percent level of accuracy over a wide range of cosmological parameters and is publicly released as part of this paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

ATEAM4Py: An Efficient and Scalable Python-Based Model for Charging Demand

This report details the development and implementation of ATEAM4Py, a Python-based simulation model that projects demand for battery electric vehicle (BEV) charging based on adoption trends and consumer behavior. With Exelon’s support, Argonne National Laboratory converted the original Java-based Agent-based Transportation Energy Analysis Model (ATEAM) into Python, resulting in a faster and more efficient tool for forecasting the timing, location, and scale of charging demand growth. ATEAM4Py tackles key challenges in simulation efficiency and runtime, supporting the strategic development of cost-effective grid capacity expansion strategies and ensuring reliable service for stakeholders.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Clumping index estimation with 30°-tilted cameras in row crops: Evaluation of methods and segment size effects

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Furthermore, applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

30°-tilted camera↗
DOE OSTI2021

Advancing Solar Innovation for Low and Moderate-Income Households (Final Technical Report)

The overarching goal of this project was to deepen the understanding of the social and economic aspects of solar adoption, with a specific eye towards any pathways or barriers that could be identified that directly impact low and moderate-income households. Specific objectives included (1) identifying and assessing significant non-economic barriers that complicate the ability of LMI households to adopt and benefit from solar, even under scenarios with low economic barriers; (2) assessing the differential ability of groups to benefit from solar adoption based on their ability to invest in different solar innovations; (3) explaining why some LMI communities nonetheless experience significantly higher rates of solar adoption and benefit than others; (4) developing a methodological model for generating data addressing key issues to the project; and (5) designing and developing an interactive web portal that aggregates existing datasets, provides researchers trend analysis capabilities, standardizes project protocols for other states and communities; provides the opportunities for researchers using the same protocol in other communities to upload their results; and engages with the general public by providing them the ability to upload their own perspectives and stories of solar technology adoption. Through a common framework for conducting semi-structured field interviews. In partnership with cooperative extension services, in conjunction with survey research and the aggregation of existing solar adoption datasets, this study has identified that currently ,there is a strong values alignment and knowledge access challenge that continues to affect solar adoption. Participants in this study – many of whom are either low or moderate-income and/or live in underserved rural communities do not believe solar is an option where they live or believe solar is appropriate for their lifestyle. This perception challenge is further exacerbated by the absolute dearth of adopters in most communities and a total lack of local marketing for solar energy. What marketing that does exist is geared towards audiences other than those in the participant's community, reinforcing a perception that 22% of survey respondents (n = 1,551) have: that solar energy is not meant for them and their community. Overcoming these serious human challenges pertaining to knowledge and marketing are critical to reducing the customer acquisition costs incurred amongst LMI communities and especially those in rural America where the opportunities solar provides to increase local energy resilience, security, all while engaging in personal stewardship of the planet could align with local values. Community-Based Social Marketing, built in partnership with community leaders and geared towards meeting local visions of ideal life, is a near term opportunity to bridge the gap between local priorities and national market and policy imperatives for solar while moving towards the administration's goals for renewable energy as part of a just and equitable decarbonization program. The tools and methods developed for this study can serve as a template for identifying other specific local challenges for solar adoption in other state, and provide the opportunity for a common data framework for understanding system-wide trends and barriers to achieving uptake of residential solar in communities that may, ultimately, benefit the most from the technology.

14 SOLAR ENERGY↗
DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Comparison of spherical harmonics method and discrete ordinates method for radiative transfer in a turbulent jet flame

Here, in this study, we systematically compared the accuracy and computational cost of two popular solution methods for the radiative transfer equation (RTE): the spherical harmonics method (P N ) and the discrete ordinates method (DOM). We first investigated convergence characteristics of different orders of P N and DOM in a series of 1D homogeneous configurations with varying optical thicknesses. Both solvers perform better for optically thicker cases. The accuracy of P N methods increases with its order, , but the gain in accuracy reduces with the increase in , i.e., improvement of P 7 over P 5 is less than that of P 3 over P 1 . This decreasing trend becomes more prominent as the optical thickness decreases. On the other hand, DOM’s accuracy increases almost linearly with the increase in the number of ordinates (or polar angles in this study) in all cases. While comparing the directional profile of radiative intensity, both solvers perform better when the radiative intensity is more isotropic. These solvers were then connected with a full spectrum k-distribution (FSK) spectral model and used to perform radiation-coupled simulations of a turbulent jet flame in an axi-symmetric cylindrical domain. Results obtained from P 1 to P 7 approximations for P N , and 2 x 4, 4 x 4, 4 x 8, 8 x 8 finite angles for DOM are compared with that from an optically thin model, and a reference solution from line-by-line (LBL) photon Monte Carlo (PMC) method. The choice of radiation solver shows a noticeable impact on the temperature distribution of the flame. The P N solvers lead to slightly higher radiant fractions and the DOM solvers lead to slightly lower radiant fractions than the PMC benchmark solution. Finally, the computational costs of each of these solvers are also reported and an intermittent evaluation / time blending scheme to improve the computational efficiency of radiation solvers in radiation-coupled simulations are also demonstrated.

42 ENGINEERING↗
DOE OSTI2026

Rapid Evaluation of Amine-Functionalized Solvents for Biomass Deconstruction Using High-Throughput Screening and One-Pot Enzymatic Saccharification

Efficient and sustainable pretreatment of lignocellulosic biomass is critical for biofuel and biochemical production, yet its optimization is often hindered by slow, labor-intensive experimental methods. Here, we report the first demonstration of a custom-built, miniaturized, high-throughput screening platform integrated with one-pot enzymatic saccharification, enabling parallel evaluation of solvent type, feedstock, and temperature with minimal material use and high reproducibility. As a proof-of-concept, the HTX platform was used to screen five amine-functionalized solvents, including isopropanolamine, butylamine, N-methylbutylamine, ethanolamine, and ethanolamine acetate across three bioenergy crops (sorghum, poplar, and switchgrass) and pretreatment temperatures ranging from 80 to 140 °C. Vacuum drying successfully removed more than 99% of the solvents from the pretreated biomass, eliminating the need for water washing prior to saccharification. Isopropanolamine and N-methylbutylamine yielded the highest glucose (70–80%) and xylose (58–67%) release, with trends reflecting feedstock recalcitrance. The produced hydrolysates supported robust growth of an engineered strain of the yeast Rhodosporidium toruloides, confirming biocompatibility. This high-throughput platform provides a scalable, feedstock-agnostic framework for rapid pretreatment screening, accelerating solvent–feedstock pairing and process optimization. Its ability to integrate pretreatment, solvent removal, saccharification, and microbial conversion in a miniaturized format offers significant advantages for cost-competitive biorefinery development.

Biomass↗
DOE OSTI2022

Cordon screen: A cordon-based congestion pricing policy evaluation method for U.S. cities

Global trends toward urbanization will exacerbate traffic congestion, delays in economic productivity, and air pollution issues for growing cities. Traffic congestion pricing is one method available to help ameliorate these concerns. New York City is on the verge of implementing a cordon-based traffic congestion pricing policy around its central business district. For budget-constrained municipalities, evaluating implementation of such policy could be costly. This article proposes a sketch-planning methodology, called Cordon Screen, for major U.S. cities to evaluate the net income, traffic mitigation, and avoided pollution emissions from cordon-based traffic congestion pricing. This method relies on national datasets and limited user-specific data inputs, along with a range of user-selectable assumptions informed by academic literature to deliver order-of-magnitude results. The numerous limitations of this method are acceptable for preliminary policy evaluation to determine if greater financial investment to obtain more accurate results is justified. The Denver metropolitan area is used to demonstrate Cordon Screen capabilities, with mid-range assumption results suggesting the policy is most effective at generating net income and increasing vehicle speeds on major interstates. For Denver, the policy is comparably less effective at reducing air pollution and increasing speeds on minor roadways. Validation against early implementation results from the London cordon are acceptable. Still, users should discount revenue generation projections. Choice of cordon area may be the most difficult obstacle when using the Cordon Screen. With refinement, Cordon Screen could serve as a low-cost, open-source planning evaluation tool for growing and congested U.S. cities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Evaluation of an autonomous acoustic surveying technique for grassland bird communities in Nebraska

Monitoring trends in wildlife communities is integral to making informed land management decisions and applying conservation strategies. Birds inhabit most niches in every environment and because of this they are widely accepted as an indicator species for environmental health. Traditionally, point counts are the common method to survey bird populations, however, passive acoustic monitoring approaches using autonomous recording units have been shown to be cost-effective alternatives to point count surveys. Advancements in automatic acoustic classification technologies, such as BirdNET, can aid in these efforts by quickly processing large volumes of acoustic recordings to identify bird species. While the utility of BirdNET has been demonstrated in several applications, there is little understanding of its effectiveness in surveying declining grassland birds. We conducted a study to evaluate the performance of BirdNET to survey grassland bird communities in Nebraska by comparing this automated approach to point count surveys. We deployed ten autonomous recording units from March through September 2022: five recorders in row-crop fields and five recorders in perennial grassland fields. During this study period, we visited each site three times to conduct point count surveys. We compared focal grassland bird species richness between point count surveys and the autonomous recording units at two different temporal scales and at six different confidence thresholds. Total species richness (focal and non-focal) for both methods was also compared at five different confidence thresholds using species accumulation curves. The results from this study demonstrate the usefulness of BirdNET at estimating long-term grassland bird species richness at default confidence scores, however, obtaining accurate abundance estimates for uncommon bird species may require validation with traditional methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

U.S. Department of Energy Solar Decathlon

The U.S. Department of Energy (DOE) Solar Decathlon® Design Challenge is a collegiate competition that challenges student teams to design high-performance buildings that push the boundaries of the industry. In the 2020 Design Challenge, DOE piloted the Design Partners Program, a low-risk opportunity for builders and building owners to harness student innovation and explore zero energy design for current or upcoming projects. Design Partners provide a student team of architects and engineers with project requirements. By the end of the Design Challenge, Design Partners receive a zero-energy design alternative and cost estimate for their project. The collaboration allows Design Partners to incorporate innovative concepts such as grid-interactivity, resilience, and low embodied carbon in a low-risk environment. It also provides the future generation of engineers and architects with invaluable experience designing a building for a client under real-world circumstances. This article summarizes the current policy, technology, health, and economic trends that make zero energy buildings desirable and feasible, and presents the value of the Solar Decathlon to industry. We highlight innovative solutions 2020 Design Partner pilot projects are bringing to the building industry.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Solid-State Transformer and Hybrid Transformer With Integrated Energy Storage in Active Distribution Grids: Technical and Economic Comparison, Dispatch, and Control

Solid-state transformer (SST) and hybrid transformer (HT) are promising alternatives to the line-frequency transformer (LFT) in smart grids. The SST features medium-frequency isolation, full controllability for voltage regulation, reactive power compensation, and the capability of battery energy storage system (BESS) integration with multiport configuration. The HT has a partially-rated converter for fractional controllability and can integrate a small BESS. Fast grid-edge voltage fluctuations from increased solar photovoltaic (PV) and electric vehicle (EV) penetration are difficult to manage for mechanical load tap changers. Hence, along with the trend towards more BESS in the grid, the controllability and the storage integration capability of the SST and HT are of strong interest. However, a review of literature shows existing SST and HT research is mostly at converter level, while system-level assessments are scarce. Assessing technical and economic impacts is critical to understanding the benefits and role of the SST and HT to guide future research, which is presented for the first time in this article. Experimental results from medium-voltage (MV) SST and MV HT prototypes are shown to confirm equipment-level feasibility, where the voltage controllability waveforms of a MV HT prototype are reported for the first time. Comparative simulations are performed on a modified IEEE 34-bus system. Here, a grid-model-less decentralized grid-edge voltage control method and a day-ahead BESS dispatch method are proposed for the SST and HT. The simulations show that the SST and HT with integrated storage can host more PV, achieve peak shaving, mitigate voltage fluctuation and reverse power flow, and support energy arbitrage for operational cost reduction, as compared to the LFT. Moreover, comprehensive analyses of net present value (NPV) and internal rate of return (IRR) are performed under different installed PV capacities, HT’s partial converter ratings, and BESS capacities. Sensitivities to future cost reductions of the PV and BESS are studied. Although the NPV and IRR are currently negative, 60% capital cost reduction or 150% revenue increase will make the SST and HT economically viable in the use case studied.

14 SOLAR ENERGY↗
DOE OSTI2026

Construction of generalized quasilinear diffusion coefficient using neural networks with physical restrictions

The quasilinear diffusion coefficient (D QL ) derived from our machine learning framework shows comparable trends with the ground truth D QL obtained from GENRAY-CQL3D simulations. Additionally, for the strong absorption cases, the radial current drive profiles generated using the D QL from our model exhibit consistent behavior with those obtained from the original simulation. These findings indicate the potential of our surrogate modeling approach with physical restrictions to replicate key wave–plasma interaction characteristics while reducing computational costs. Traditionally, calculating D QL for wave–particle interactions relies on computationally intensive wave simulations coupled with Fokker–Planck solvers. To address this challenge, we developed a machine learning-based surrogate model with physical restrictions derived from cold plasma theory and bounce-averaged damping effects. First, we establish the propagation domain of Lower Hybrid Waves in the (N∥, ρ) space by identifying the accessibility limit and determining the upper and lower bounds of N∥ using the Potential Power Deposition (PPD) method. Subsequently, leveraging a database constructed using Latin hypercube sampling alongside the underlying physical restrictions (e.g. PPD), machine learning methods including U-Net and Recurrent Neural Networks are employed to design a physics-restricted machine learning framework capable of reconstructing D QL .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Solar Energy from a Big Picture Perspective to Nanoscale Insights via TOF-SIMS

The world is undergoing a rapid transformation in the ways that we generate and store energy. This has been driven not only by concerns about the climate but by simple economic factors due to the dramatic cost decreases in wind in solar power. In most places of the world where one would now want to build a new power plant, the cheapest option is to use wind of solar for power generation. Abundant clean energy when the sun shines most is driving new research for daily and seasonal energy storage in many different technologies. Here, we will briefly these discuss energy trends as a whole, before diving into our recent contributions to the field using time-of-flight secondary-ion mass spectrometry (TOF-SIMS) to improve the performance and reliability of solar cells.

14 SOLAR ENERGY↗
DOE OSTI2022

Bridging Equipment Reliability Data and Robust Decisions in a Plant Operation Context

In order to reduce operation and maintenance (O&M) costs, nuclear power plants (NPPs) are moving from corrective and periodic maintenance to predictive maintenance strategies. Such transition requires changes on the data that needs to be retrieved and on the type of decision processes to be employed. Advanced monitoring and data analysis technologies are essential to support predictive strategies. They can in fact provide precise information about health of a component, track its degradation trends, and provide information of its expected failure time. With such information, maintenance operations for a component can be performed right before its expected failure time. This dynamic context of O&M operations requires new methods to analyze data, propagate component health information from the component to the system level, and optimize plant resources. In this respect, the risk informed asset management (RIAM) project has been tasked to develop and test this new class of methods into a risk analytics toolset. This toolset consists of data analytics tools coupled with reliability methods designed to manage plant assets and performances in a predictive maintenance context. This report shows the latest improvements on such development and the initial testing of our methods on the three main research areas that the RIAM project is focusing on. These areas are the following: equipment reliability data analytics, system reliability modeling, and plant resources optimization methods. We show how the methods developed in these areas can support predictive maintenance strategies by: 1) analyzing equipment reliability data (either in numeric and textual form), 2) assessing component and system health through an innovative margin-based reliability approach, and 3) identifying the most critical components and set optimal maintenance schedule based on plant economic and operational constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Novel Experimental Approach to Understand the Transport of Nanodrugs

Nanoparticle-based drugs offer attractive advantages like targeted delivery to the diseased site and size and shape-controlled properties. Therefore, understanding the particulate flow of the nanodrugs is important for effective delivery, accurate prediction of required dosage, and developing efficient drug delivery platforms for nanodrugs. In this study, the transport of nanodrugs including flow velocity and deposition is investigated using three model metal oxide nanodrugs of different sizes including iron oxide, zinc oxide, and combined Cu-Zn-Fe oxide synthesized via a modified polyol approach. The hydrodynamic size, size, morphology, chemical composition, crystal phase, and surface functional groups of the water-soluble nanodrugs were characterized via dynamic light scattering, transmission electron microscopy, scanning electron microscopy-energy dispersive X-ray, X-ray diffraction, and fourier transform infrared spectroscopy, respectively. Two different biomimetic flow channels with customized surfaces are developed via 3D printing to experimentally monitor the velocity and deposition of the different nanodrugs. A diffusion dominated mechanism of flow is seen in size ranges 92 nm to 110 nm of the nanodrugs, from the experimental velocity and mass loss profiles. The flow velocity analysis also shows that the transport of nanodrugs is controlled by sedimentation processes in the larger size ranges of 110–302 nm. However, the combined overview from experimental mass loss and velocity trends indicates presence of both diffusive and sedimentation forces in the 110–302 nm size ranges. It is also discovered that the nanodrugs with higher positive surface charges are transported faster through the two test channels, which also leads to lower deposition of these nanodrugs on the walls of the flow channels. The results from this study will be valuable in realizing reliable and cost-effective in vitro experimental approaches that can support in vivo methods to predict the flow of new nanodrugs.

36 MATERIALS SCIENCE↗