Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model development”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Transport and confinement physics: Chapter 2 of the special issue: on the path to tokamak burning plasma operation

Progress in physics understanding and theoretical model development of plasma transport and confinement (TC) in the ITPA TC Topical Group since the publication of the ITER Physics Basis (IPB) document (Doyle et al 2007 Nucl. Fusion 47 S18) was summarized focusing on the contributions to ITER and burning plasma prediction and control. This paper provides a general and streamlined overview on the advances that were mainly led by the ITPA TC joint experiments and joint activities for the last 15 years (see JEX/JA table in appendix). This paper starts with the scientific strategy and scope of the ITPA TC Topical group and overall picture of the major progress, followed by the progress of each research field: particle transport, impurity transport, ion and electron thermal turbulent transport, momentum transport, impact of 3D magnetic fields on transport, confinement mode transitions, global confinement, and reduced transport modeling. Cross references with other Topical Groups are given in order to highlight overlapped topics, such as the 3D effect on the plasma transport in the edge and L-H transition physics. The increasing overlap between the topical groups is a reflection of the progress on integrating the known physics into comprehensive models that are better and better able to reproduce the plasma transport. In recent years, such integration has become increasingly prevalent when considering transport from the SOL, through the edge pedestal, and into the plasma core. In the near future, increased collaboration also with the magneto-hydrodynamic and energetic particles community will be important as we approach burning plasma conditions in next-step fusion devices. A summary of remaining challenges and next steps for each research field is given in the Summary section.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Testing of High S Matrix Glasses to Expand DFHLW Glass Compositional Ranges (Rev.1)

Gaps in glass composition-property data for direct-feed high-level waste (DFHLW) have recently been identified. One such gap is the region of high sulfur solubility since previous, pretreated, high-level wastes contained very little sulfur. Filling this data gap will significantly broaden the range of process flowsheet options including minimal washing and will allow for optimized waste loading in DFHLW glasses. This report summarizes the data collected during the characterization of the DFHLW High S Glass Matrix (HS24). A glass matrix of 50 glass compositions was developed to evenly cover the DFHLW composition region for high sulfur glass. Matrix glasses were designed to expand the composition region outside the current component concentration and property limits so as to reduce uncertainties at the limits. The 50 matrix glasses were fabricated and tested for properties important to the success of DFHLW vitrification including: compositions, canister centerline cooling (CCC) crystallinity and isothermal crystallinity, density, viscosity, electrical conductivity (EC), product consistency test (PCT) response, toxicity, and sulfate solubility. Melter materials corrosion testing is reported elsewhere. These glasses were intentionally designed to have high SO 3 solubilities (0.7 to 2.2 SO 3 wt%) in compositional regions that had not been previously explored. Forty-eight glasses showed the measured SO 3 content retained >80% of the target SO 3 and the densities of all the glasses ranged from 2.49 g·cm -3 to 2.74 g·cm -3 . While the model predicted nepheline formation in 5 glasses, one of the tested 50 CCC glasses formed nepheline, and 35 glasses formed Cr-containing phases such as spinels and eskolaite. Only five glasses were amorphous after CCC treatment where 44 glasses with detectable crystals contained =10 wt% crystals and only one glass had > 10 wt% crystals. None of the glasses exceeded the allowable T 2% for spinel crystal formation at 950 ºC (i.e., no glasses had >2 wt% spinel at 950 ºC) during isothermal crystal fraction tests where 10 glasses showed no crystalline phases at or below 950 ºC. All the glasses (except one which failed being slightly lower than the target) satisfied the SO 3 constraint while 98 glasses did not meet the viscosity constraints and 4 failed the EC constraints. Six quenched (Q) and six CCC glasses failed the Defense Waste Processing Facility (DWPF) Environmental Assessment (EA) glass PCT threshold and 3 Q and 4 CCC failed the PCT design constraint. One glass exceeded the WTP delisting limits for Cr via EPA Method 1311 (i.e., Toxicity Characteristic Leaching Procedure, TCLP). It should be emphasized that some of these glasses were specifically designed to approach or even exceed certain property constraints, as filling data gaps in these regions will provide the greatest benefit for future model development by improving accuracy and reducing uncertainties. These insights will ultimately support the development of more robust glass formulation strategies, enabling higher waste loading, reducing operational risks, and expanding the processing envelope.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Virtual refrigerant charge sensing algorithm for residential CO₂ heat pumps

Natural refrigerants are increasingly adopted in next-generation heat pump systems, among which CO₂ heat pumps have attracted significant attention. However, due to their high operating pressures, the leakage risk is higher, resulting in undercharge conditions and degraded heat pump performance. Thus, developing an accurate refrigerant charge level detection technique is necessary to guarantee safe and efficient operation. Although virtual refrigerant charge (VRC) level calculation algorithms for CO₂ heat pumps exist, they typically rely on empirically selected features without a systematic selection framework, leading to multicollinearity and potential overfitting, which limit their prediction accuracy and generalizability. To address these issues, this study proposes a VRC algorithm framework with a systematic feature selection method that identifies physically meaningful and statistically significant features, and is applied using a residential CO₂ heat pump as a case study. The method is extended from previous work on conventional refrigerants to account for charge behavior in CO₂ gas coolers. The selected features include gas cooler outlet density, evaporator pressure, and superheat temperature. The results demonstrate that the proposed feature selection method significantly improves prediction accuracy compared to existing VRC approaches. A relatively small training dataset (∼30 samples) is sufficient for feature identification and model development. The developed algorithm achieves less than 3% prediction error under both undercharge and overcharge conditions, representing reductions of 46.7% and 35.3% compared to two recent reference VRC algorithms for transcritical CO₂ heat pumps reported in the literature. The proposed algorithm and feature selection method enhance leakage detection capability, facilitate the deployment of CO₂ heat pump systems, and contribute to reduced energy waste and maintenance costs.

Guo, Fangzhou [Lawrence Berkeley National Laborato↗
DOE OSTI2026

Coupled heat and mass transfer in internally cooled silica gel-coated aluminum foam heat exchangers for dehumidification

Solid desiccant dehumidifiers offer significant potential to improve humidity control in buildings due to their ability to independently regulate latent (humidity) and sensible (temperature) loads. However, adsorption of water vapor by desiccants is an exothermic process; the resulting heat of adsorption increases both the desiccant bed temperature and the temperature of the air flowing over the bed, thereby limiting the performance of desiccant dehumidifiers. To address this problem, desiccant-coated heat exchanger prototypes were developed by coating silica gel onto aluminum foam substrates and integrated with chilled water circulation for effective removal of the heat of adsorption. Prototype heat exchangers with two different pore densities (10 and 20 pores per inch (PPI)) and two different desiccant loadings (∼10.36% and ∼17.80%) were fabricated. Experiments were conducted by varying the inlet air face velocity, chilled water inlet temperature, and chilled water flow rate to investigate the coupled heat and mass transfer characteristics of the prototype heat exchangers over a wide range of operating conditions. The underlying transport mechanisms were analyzed using transient desiccant bed temperature, outlet air temperature, and outlet humidity profiles. The results demonstrate that internal cooling significantly enhances dehumidification performance by maintaining more favorable thermodynamic conditions for water vapor adsorption. Under identical operating conditions, a 20 PPI heat exchanger coated with 25.05 g of silica gel achieved 98.98% higher cumulative water uptake and 97.75% higher moisture removal capacity than the corresponding non-cooled case after 5 min of adsorption. This substantial enhancement was further supported by a validated one-dimensional coupled heat and mass transfer model developed in the present study. To date, this is one of the first experimental studies to systematically investigate the combined effects of internal cooling, foam pore density, and desiccant loading on the performance of silica-gel-coated metal foam heat exchangers under HVAC-relevant operating conditions.

42 ENGINEERING↗
DOE OSTI2026

The scientific case for concurrent neutron and X-ray scattering and spectroscopy

The interrogation of materials with X-rays or neutrons to determine structure, energetics, and dynamics is fundamental to advancing physical and chemical materials science and enabling innovative material technologies. A persistent challenge in materials development is that progress depends on understanding structure and dynamics across multiple length and time scales in increasingly complex, multicomponent systems featuring interfaces, heterogeneity, and hierarchical organization. Despite rapidly growing demands on materials characterization, current experimental approaches are almost exclusively based on isolated X-ray or neutron scattering and spectroscopy, reflecting a paradigm largely unchanged for decades. To assess the scientific need for a new experimental paradigm, a 3-day workshop sponsored by the U.S. National Science Foundation (NSF) was held at the SpringHill Suites, San Jose, California, from June 2 to 4, 2022. The workshop brought together 70 national and international experts who critically evaluated opportunities enabled by concurrent neutron and X-ray (NeX) scattering, spectroscopy, and imaging experiments. The participants reached a clear consensus that establishing NeX capabilities is crucial for advancing the science of complex materials in the United States. This report illustrates the scientific drivers for NeX experiments through representative examples spanning biomaterials, energy materials, soft matter, nanomaterials, quantum materials, geoscience, and applied materials research. The complementarity of neutrons and X-rays is essential for robust model development and refinement, particularly in multiphase and multicomponent systems. While joint refinement of data from separate experiments is valuable, concurrent measurements uniquely eliminate uncertainties arising from sample evolution, environmental drift, and irreproducibility associated with experiments performed at different locations and times. Realizing NeX capabilities will require the development of new instrumentation, data analysis frameworks, and robust sample environments compatible with both neutron and X-ray probes. Addressing these challenges will enable unambiguous interpretation of complex materials behavior and open new frontiers in materials research.

X-ray↗
DOE OSTI2024

To Exascale and Beyond—The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), a Performance Portable Global Atmosphere Model for Cloud-Resolving Scales

The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional “high–resolution” simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud-Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real-world climate via a set of four 40 day simulations covering all 4 seasons of the year.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives.

access↗
DOE OSTI2026

Plutonium migration and phase evolution in irradiated U-Pu-Zr metallic fuels: An integrated EPMA-SEM-TEM study

Constituent redistribution is a defining feature of irradiated U-Pu-Zr metallic fuels, yet its mechanisms and effects on fuel performance are not sufficiently resolved to guide model development. Although decades of irradiation testing have established broad trends, a true mechanistic understanding of constituent redistribution has not been achieved. Here, in this study, we use electron probe microanalysis (EPMA), scanning electron microscopy (SEM), and transmission electron microscopy-based (TEM) selective area electron diffraction (SAED) on a EBR-II irradiated U-19 wt.% Pu-6 wt.% Zr fuel pin cross-section to correlate the composition, porosity, and crystallographic phases formed after irradiation. Constituent redistribution is thought to consist of three distinct zones, in which uranium and zirconium migrate while plutonium remains relatively unchanged. Our EPMA results resolve eight distinct compositional regions, and more importantly, show that plutonium redistributes alongside zirconium, contrary to historical assumptions. The distribution of fission products was highly asymmetric with a few large lanthanide precipitates observed at isolated sites on the pin periphery instead of a uniform distribution of smaller precipitates around the periphery. Using thermodynamic data from TAF-ID and the measured EPMA compositions, matrix phase fractions were predicted across the fuel radius. Phase predictions based on composition did not match TEM/SAED results, which revealed a much higher fraction of α−U phase than would be expected if phases were retained from reactor temperatures. These findings highlight the need for expanded SAED phase identification to capture post-irradiation and storage effects, as well as rigorous uncertainty quantification in fuel performance and phase diagram modeling to better constrain predictions from compositional data.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Understanding carbon sourcing and transport originating from the helicon antenna surfaces during high-power helicon discharge in DIII-D Tokamak

The high-power helicon wave system in the DIII-D tokamak could potentially introduce new plasma–material interaction (PMI) challenges owing to rectified RF sheath potentials that develop near the antenna and surrounding plasma-facing components. We present the first application of the STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to helicon-induced PMIs, extending previous STRIPE studies of ICRH antennas by incorporating net erosion, local re-deposition, and three-dimensional global impurity transport. The integrated workflow couples SOLPS, COMSOL, RustBCA, GITR, and GITRm to simulate carbon erosion, re-deposition, and impurity transport for two experimentally constrained DIII-D H-mode helicon operating scenarios with different antenna–plasma gaps, coupled RF powers, and edge plasma conditions. COMSOL predicts rectified RF sheath potentials of 1–5 kV localized near the lower portion of the antenna, where the magnetic field intersects the surface at grazing incidence. Carbon self-sputtering dominates the erosion source, whereas RF-accelerated D + ions contribute approximately 1% of the total gross erosion. The smaller-gap operating scenario exhibits substantially stronger gross erosion, enhanced local re-deposition (∼12%), and a larger confined carbon inventory owing to increased plasma accessibility and broader RF sheath coverage. Comparison with available DIII-D measurements shows no distinct change in the global carbon signal that correlates with the helicon RF pulse, consistent with the simulations indicating that the helicon-generated carbon source remains small compared with the existing background carbon inventory under the present graphite-wall operating conditions. These results demonstrate the capability of STRIPE to integrate RF sheath modeling, plasma transport, surface interaction physics, and three-dimensional impurity transport for the interpretation of helicon-induced PMIs. The study further identifies the principal sources of modeling uncertainty, including grazing-angle RF sheath physics, slow-wave resolution, plasma-background extrapolation, and trace-impurity transport assumptions, providing a framework for future validation and model development.

Kumar, Atul [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2025

First principles free energy model with dynamic magnetism for δ -plutonium

We present an ab initio free energy model derived from a fully relativistic density functional theory (DFT) electronic structure with dynamic magnetism for δ -plutonium (face-centered cubic, fcc). The DFT model is extended with orbital-orbital interaction in a parameter free orbital polarization (OP) mechanism consistent with previous modeling of plutonium. Gibbs free energy is built from components associated with the temperature dependence of the electronic structure and the corresponding electronic entropy, lattice vibrations within an anharmonic lattice dynamics model, and dynamical fluctuations of the magnetization density, i.e. magnetic fluctuations. The fluctuation model consists of transverse and longitudinal modes driven by temperature induced excitations of the DFT + OP electronic structure. The ab initio model thus incorporates fluctuating states beyond the electronic ground state. Thanks to the dynamic magnetism, the theory predicts excellent thermodynamic properties and a Gibbs free energy in accord with CALPHAD and semi-empirical modeling developed from the thermodynamic observables. The magnetic fluctuations further explain anomalous behaviors of the thermal expansion in plutonium. Specifically, a thermal expansion for the δ -plutonium system turning from positive to negative at temperatures above room temperature, a tendency for gallium to reduce and remove the negative thermal expansion depending on composition, and a positive thermal expansion for the high temperature ϵ phase.

dynamic↗
DOE OSTI2025

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Alloying of Mn with Bi‐Rich Surfaces of Bi 2 Te 3 by Topotactic Reaction

Interfacing topological insulators (TI) with magnetic materials enables accessing quantum effects for advanced devices. The synthesis of such heterostructures faces challenges due to interlaying mixing and the often-complex multicomponent materials required to combine the desired properties. An alternative synthesis to direct growth is the modification of 2D materials by topotactic reactions to introduce new functionalities into pre-formed single or few-component 2D sheets. Here, the self-formation of bismuthene (a bilayer of Bi; Bi 2 ) is utilized by thermal decomposition of Bi 2 Te 3 as a platform to create novel 2D overlayers on the TI- substrate. Specifically, the Bi 2 layer is modified by reacting it with vapor deposited Mn in an attempt to induce magnetic properties. Scanning tunneling microscopy indicates that the Mn modified surface remains in a planar 2D layer, indicating the formation of an ordered Mn-rich surface layer. Density functional theory (DFT) is used to develop models of both the Bi-rich Bi 2 Te 3 surface as well as their subsequent Mn-modified structure. The DFT models indicate the possibility of magnetic ordering of the high spin Mn 2+ ions. The successful synthesis of planar MnBi alloys on a van der Waals material illustrates topotactic reactions as an alternative for creating novel layered heterostructures.

2D materials↗
DOE OSTI2025

Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population

This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. We used a DenseNet121 model pre-trained MIMIC-CXR dataset for deep learning-based multi-label classification using ground truth labels from radiology reports extracted using the CheXpert and CheXbert Labeler. We compared the performance of the 14 chest X-ray labels on the MIMIC-CXR and Veterans Healthcare Administration chest X-ray dataset (VA-CXR). The validation of ground truth and the assessment of multi-label classification performance across various NLP extraction tools revealed that the VA-CXR dataset exhibited lower disagreement rates than the MIMIC-CXR datasets. Additionally, there were notable differences in AUC scores between models utilizing CheXpert and CheXbert. When evaluating multi-label classification performance across different datasets, minimal domain shift was observed in the unseen VA dataset, except for the label “Enlarged Cardiomediastinum.” The subgroup with the most significant variations in multi-label classification performance was study year. These findings underscore the importance of considering domain shift in chest X-ray classification tasks, paying particular attention to the temporality of the exam. Our study reveals the significant impact of domain shift and demographic factors on chest X-ray classification, emphasizing the need for improved transfer learning and robust model development. Addressing these challenges is crucial for advancing medical imaging research and improving patient care.

chest X-ray image classification↗
DOE OSTI2026

Do we have globally representative data to understand soil processes?

Understanding and modeling soils and soil organic matter (SOM) are central to a variety of human needs, from food production to ecosystem management. Soil data have been collected for over a century, but the global spatial and process representativeness of soil data remains unclear. We assessed the representativeness of currently available soil data that could be used to understand a variety of SOM processes. We used 16 open-source soil databases and data from over 281,000 unique locations globally, categorizing the databases into three main data types necessary to understand SOM processes: soil carbon stocks and fluxes, mechanistic drivers of these stocks and fluxes, and soil carbon gain or loss potential. We found that stock and driver data have extensive global coverage. However, data on soil carbon gain or loss potential, particularly data describing change in soils over time such as time series data, are severely limited in their global coverage. We conclude that while significant strides have been made in measuring soil carbon stocks and fluxes, and their drivers, we are limited in global data related to changes in soils over time. Our recommendations for soil data generators are to ensure precise metadata reporting and prioritizing sampling in underrepresented areas like tropical, arctic, mountainous, wetland and arid regions. We also encourage designing revisit schemes that explicitly support change detection and reporting multi-modal datasets that can aid in model development. Targeted measurement of low coverage soil data types and regions is necessary for a range of applications including current and future biogeochemical predictions, and their management and policy implications.

carbon fluxes↗
DOE OSTI2024

Sensory integration for neuroprostheses: from functional benefits to neural correlates

In the field of sensory neuroprostheses, one ultimate goal is for individuals to perceive artificial somatosensory information and use the prosthesis with high complexity that resembles an intact system. To this end, research has shown that stimulation elicited somatosensory information improves prosthesis perception and task performance. While studies strive to achieve sensory integration, a crucial phenomenon that entails naturalistic interaction with the environment, this topic has not been commensurately reviewed. Therefore, here we present a perspective for understanding sensory integration in neuroprostheses. First, we review the engineering aspects and functional outcomes in sensory neuroprosthesis studies. In this context, we summarize studies that have suggested sensory integration. We focus on how they have used stimulation-elicited percepts to maximize and improve the reliability of somatosensory information. Next, we review studies that have suggested multisensory integration. These works have demonstrated that congruent and simultaneous multisensory inputs provided cognitive benefits such that an individual experiences a greater sense of authority over prosthesis movements (i.e., agency) and perceives the prosthesis as part of their own (i.e., ownership). Thereafter, we present the theoretical and neuroscience framework of sensory integration. We investigate how behavioral models and neural recordings have been applied in the context of sensory integration. Sensory integration models developed from intact-limb individuals have led the way to sensory neuroprosthesis studies to demonstrate multisensory integration. Neural recordings have been used to show how multisensory inputs are processed across cortical areas. Lastly, we discuss some ongoing research and challenges in achieving and understanding sensory integration in sensory neuroprostheses. Here, resolving these challenges would help to develop future strategies to improve the sensory feedback of a neuroprosthetic system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

A quality-agnostic combinatoric cost estimation model for large-format directed energy deposition metal additive manufacturing

Directed energy deposition (DED) additive manufacturing (AM) processes are amenable to synergistic combination into multi-process AM systems due to similar requirements for automation and energy sources. This work analyzes the economic performance of such DED AM systems from a quality-agnostic combinatoric standpoint with a model that calculates lowest-cost system combinations based on part geometry and process performance metrics. Common DED AM systems research focuses on a single process and does not consider the process, system, and application in the context of all possible system combinations (e.g., the combined set of process selection(s), motion system(s), and process hardware), leading to limited applicability of the resulting DED AM systems to cost-sensitive components such as those found in energy generation applications. The model developed herein incorporates the capital, material, and energy costs associated with DED AM system combinations into a predictive tool for estimating part and system cost, the output of which is intended to guide deployment of finite research and development resources towards DED AM system combinations with the lowest costs and greatest likelihood of economic impact. The DED AM systems identified by this framework may enable domestic production of the large conventionally cast and forged components necessary for energy generation.

Shanafield, Alexandra [ORNL]↗
DOE OSTI2025

Permeate fluxes from desalination of brines and produced waters: A reactive transport modeling study

The increasing interest in the use of membrane systems to desalinate inland brackish water, agricultural drainage, and industrially produced wastewater demands improved means of predicting desalination system performance under variable feedwater compositions. The interaction among water flow, solute transport, and chemical composition in these systems impacts permeate flux evolution. Here, an established multicomponent reactive transport simulator that accounts for these coupled processes is applied to compute osmotic pressure and permeate fluxes in reverse osmosis (RO) systems. The model is first validated by predicting permeate fluxes for a set of benchtop crossflow experiments subject to a range of feed flow rates and compositions, under fouling and non-fouling conditions. Results compare favorably with measured data that show that solutions with similar total dissolved solids concentrations but different compositions result in different permeate fluxes. The model is then applied to predict permeate fluxes from the desalination of produced waters using a commercial spiral wound RO module. For NaCl-dominant brines, at total dissolved salt concentrations (TDS) below about 70 g/L, permeate fluxes are inversely proportional to water mole fraction as the latter is a reasonable approximation of water activity (i.e. ideal mixing). In the case of Ca–Cl-, Na–CO3- and Na–SO4-dominant brines below about 70 g/L TDS, this relationship does not hold as well and tends to overpredict osmotic pressure and thus underpredict permeate fluxes. However, the opposite becomes true at higher TDS values for typical produced waters. The scaling potential of these waters is also computed by allowing the precipitation of minerals above their saturation limit on the RO membrane. This work demonstrates how reactive transport models developed for the analysis of waters from geological systems can be extended to improve process design, optimization, and control in desalination systems from produced waters and beyond.

Molins, Sergi↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗