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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Recent Progress on the Stochastic Objective Decision Aide (SODA) Application

Idaho State University, in collaboration with Idaho National Laboratory, is engaged in development of a simple to use software application that utilizes stochastic techniques for the calculation of radiation doses associated with hypothetical radioactive material releases. This work is being performed through a grant from the U.S. Department of Energy (DOE) Nuclear Safety Research and Development (NSR and D) Program. Initial progress on the application was reported previously. Recent additions to the application are reported here. The Stochastic Objective Decision Aide (SODA) provides an easy to use software application to help decision makers support their judgment about the need or lack of need for safety structures, systems, and components. SODA is not intended to replace traditional design basis accident dose calculation methodology, nor is it intended to replace existing software used to perform formal dose consequence calculations. Rather, SODA is intended to supplement information obtained from existing codes to give a decision maker more information about an accident scenario. SODA also provides a simple method to perform parametric or sensitivity studies on individual input parameters. Non-reactor nuclear facilities operated under the approval authority of the US DOE use unmitigated hazard evaluations to determine if potential radiological doses associated with design basis events challenge dose evaluation guidelines. Unmitigated events that sufficiently challenge dose evaluation guidelines merit the selection of safety structures, systems, or components (SSCs) to prevent or mitigate the hazard. Dose consequence calculations traditionally involve use of the 'five-factor' formula: ST = MAR.DR.ARF.RF.LPF where ST is the source term (Bq), MAR is the total available material-at-risk (Bq), DR is the damage ratio (no units), ARF is the airborne release fraction (no units), RF is the respirable fraction (no units), and LPF is the leak path factor (no units). Potential radiation doses are then calculated using: CED = χ/Q.BR.ST.DCF (2) where CED is the committed effective dose (Sv), χ/Q is the plume dispersion (s/m{sup 3}), BR is the breathing rate (m{sup 3}/s), ST is the source term (Bq), and DCF is the dose conversion factor (Sv/Bq). Conservative single value input parameters are typically used to represent ARF, RF, LPF and BR. The traditional methodology, while conservative, can lead to skewed conclusions in the balance between cost and risk reduction resulting in over engineered systems with greater design, construction and operating costs. Rather than using a bounding single point value for each parameter in the dose consequence calculation, SODA uses distributions for some or all of the parameters. Each input parameter distribution is stochastically sampled and the resulting dose consequence calculation is repeated many times to develop a dose consequence distribution. The resulting distribution provides more information for the decision maker to rely upon when determining the need for safety SSCs. The SODA application was developed using MATLAB and it incorporates the use of Monte Carlo techniques as well as a graphical user interface (GUI). The application includes user selection of the governing distribution for parameters such as the MAR, DR, ARF, RF, LPF,χ/Q, BR, and DCF. While MATLAB was used for the development work, the application is distributed as a self-contained executable program. Features of the application include pull down menus with available distributions for the various parameters. The user has the option to plot the input parameter distribution that results from the random sampling process. The resulting dose consequence distribution is automatically analyzed to determine its similarity to well know distributions. This was done using the Bayesian Information Criterion method. If the resulting distribution is sufficiently similar to a well-known distribution, the application provides information about the distributions such as the mean value and other parameters. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Improving the User Interface of the DeepLynx Data Warehouse

DeepLynx is an open-source ontology-based data warehouse created by INL to support the creation and life cycle of digital engineering projects, with a particular emphasis on digital twins [1]. Digital twins are systems that represent physical assets and process in a real-time digital environment [1]. Most well-known commercial data warehouses use Graphical User Interfaces (GUIs) for users to interact with their systems [3]. Limited publications have addressed the design of these interfaces and understanding of their target users. The current users and development team acknowledge the need to improve the current UI, not just for aesthetics but to improve functionality and workflow of DeepLynx. Traditional data warehouse users are developers, data scientists and business analysts [2]. DeepLynx users have a vast range of experience using data warehouses, and diverse roles, including engineers, scientists and management positions. Because there is a broader audience of target users for DeepLynx than a typical data warehouse, it is essential that DeepLynx has a useable and intuitive user interface. To achieve this the team performed human-computer interaction methods, including a Heuristic Evaluation of current UI using Neilsen’s Usability Heuristic, create personas based on current users by designing a user survey, data analysis and develop of personas. Followed by a redesign of the UI following using Neilsen’s Usability Heuristic and Norman’s Principles of Interactive Design in industry standard software Figma. Lastly a Heuristic Evaluation of new UI design, using Neilsen’s Usability Heuristic and User testing of redesign UI and have a group of users complete a Thinking Aloud Test of the new UI. Preliminary results of the Heuristic Evaluation of current UI arise issue with Consistency and Standards, Visibility of System Status, Match System and Real World and Recognition Rather than Recall. These issues were addressed in the proposed redesign by applying Neilsen’s Usability Heuristic and Norman’s Principles of Interactive Design. Next steps include formalized list of lessons learned and design implications for future publications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Automated Detection of Qubit Structures on Quantum Chips

In this study, YOLO (You Only Look Once), a well-known object detection and image segmentation model, is used to detect qubits on a quantum chip. The model is trained by exposing it to various images of objects of interest and by tweaking various training parameters to allow the model to learn from a limited and highly specialized dataset of SEM imagery. The model was provided with numerous images of qubits and various prominent features of note located on a qubit. These detections were then integrated into a simple GUI, allowing for real-time feedback alongside SEM usage. This will allow us to create various functions enabling automated functions like chip-wide scanning, auto-focusing, and image correction.

Perjuste, Ruth [Wellesley Coll.]↗
DOE OSTI2026

Natural Language Processing to Inform Agent-Based Modeling: With Application to Modeling Adoption of Medium-Duty Electric Vehicles

Agent-based socio-technical modeling of medium- and heavy-duty (MDHD) electric vehicle (EV) adoption has the potential to provide analysis, prediction, and gui. This paper describes new applications of text analysis developed through machine learning (ML) to build and understand relevant topics and their saliency in the published discourse on adoption of MDHD EVs. This work contributes to the state of the art in topic mining models by defining a new metric of topic ranking (START) that quantifies the importance of predefined topics within the corpus using weighted results for predefined topics from two topic modeling approaches: Latent Dirichlet Allocation (LDA) and BERTopic. The START metric is then demonstrated in practice to model how academia and industry view the EV adoption process based on the respective texts published by these groups. Results show that academic literature places more emphasis on categories of interests such as norms/attitudes and adopter knowledge, while trade journals tend to emphasize long-term cost more than academia. The two bodies of literature agree on the importance of policy and incentives in MDHD EV adoption. Together these results illustrate the potential to use ML-based text analysis to populate the characteristics of agent-based socio-technical models.

Electric vehicle adoption, fleet electrification, ↗
DOE OSTI2025

Work updates for Prof. Lee

I want to update my work which I have done so far in INL, including matlab GUI for neutron radiography and some fuel EBSD works.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2023

MiGRIDS

MiGRIDS is a software that models islanded microgrid power systems with different controls and components. For example, using load and resource data from a microgrid, you could model it with additional wind turbines, battery etc. You could also try out different dispatch schemes to see which one worked best. MiGRIDS is designed to help optimize the size and dispatch of grid components in a microgrid. While a grid connect feature is expected to be added in the future, islanded operation is the focus. Note that this is a basic implementation and more features and functionality (such as a GUI) are coming! MiGRIDS runs time-step energy balance simulations for different grid components and controls. In smaller microgrid environments, dispatch decisions are being made on the order of seconds. In order to fully capture their effect, this tool lets you run simulations on the order of seconds. The end result is a more realistic representation of what can be achieved by integrating different components and control strategies in a grid.

Morgan, Tawna↗
DOE OSTI2015

Computer model for electrochemical cell performance loss over time in terms of capacity, power, and conductance (CPC)

Available capacity, power, and cell conductance figure centrally into performance characterization of electrochemical cells (such as Li-ion cells) over their service life. For example, capacity loss in Li-ion cells is due to a combination of mechanisms, including loss of free available lithium, loss of active host sites, shifts in the potential-capacity curve, etc. Further distinctions can be made regarding irreversible and reversible capacity loss mechanisms. There are tandem needs for accurate interpretation of capacity at characterization conditions (cycling rate, temperature, etc.) and for robust self-consistent modeling techniques that can be used for diagnostic analysis of cell data as well as forecasting of future performance. Analogous issues exist for aging effects on cell conductance and available power. To address these needs, a modeling capability was developed that provides a systematic analysis of the contributing factors to battery performance loss over aging and to act as a regression/prediction platform for cell performance. The modeling basis is a summation of self-consistent chemical kinetics rate expressions, which as individual expressions each covers a distinct mechanism (e.g., loss of active host sites, lithium loss), but collectively account for the net loss of premier metrics (e.g., capacity) over time for a particular characterization condition. Specifically, sigmoid-based rate expressions are utilized to describe each contribution to performance loss. Through additional mathematical development another tier of expressions is derived and used to perform differential analyses and segregate irreversible versus reversible contributions, as well as to determine concentration profiles over cell aging for affected Li+ ion inventory and fraction of active sites that remain at each time step. Reversible fade components are surmised by comparing fade rates at fast versus slow cycling conditions. The model is easily utilized for predictive calculations so that future capacity performance can be estimated. The invention covers mathematical and theoretical frameworks, and demonstrates application to various Li-ion cells covering test periods that vary in duration, and shows model predictions well past the end of test periods. Version 2.0 Enhancements: the code now covers path-dependent aging scenarios, wherein the framework allows for arbitrarily-chosen aging conditions over a timeline to accommodate prediction of battery aging over a multiplicity of changing conditions. The code framework also allows for cell conductance and power loss evaluations over cell aging, analysis of series strings that contain a thermal anomaly (hot spot), and evaluation of battery thermal management parameters that impact battery lifetimes. Lastly, a comprehensive GUI now resides in the Ver. 2.0 code.

Gering, KevinL.↗
DOE OSTI2015

Event-modeled Risk Assessment Using Linked Diagrams

In order to develop a dynamic probabilistic risk assessment capability, a prototype discrete event simulator was created as part of an LDRD that can model dynamic systems and generate probabilistic risk/reliability results. The development consisted of three major parts: 1. Development of a discrete event modeling software library 2. Design of a simulation object model for dynamic modeling 3. Construction of the graphical user interface (GUI) that will allow risk assessment analysts to more easily develop these models We called this new tool EMRALD. A key part of the EMRALD tool has been to develop an object-oriented software model that is flexible enough to support the varied dynamic simulation models (e.g., fails to operate, fails on demand). An object-oriented approach better matches the simulation model to real world objects and makes it more intuitive for the modeler. The interface represents actual physical components, specifically tracking how they interact. EMRALD is a discrete-event simulation tool that is coupled with a state-based diagramming graphical user interface. A key part of the EMRALD tool has been to develop an object-oriented software model that is flexible enough to support the varied dynamic simulation models (e.g., fails to operate, fails on demand). By having a state-based approach, we can integrate different hazards into a single comprehensive model. For example, a single model can include fire, flooding, transient, and seismic initiating events -- each of these events becomes a trigger into the state-based approach that tell the model to make a transition based upon the specific initiator. An object-oriented approach better matches the simulation model to real world objects and makes it more intuitive for the modeler. The interface represents actual physical components, specifically tracking how they interact.

Smith, CurtisLee↗
DOE OSTI2020

TiO2 Nanocrystal-Framed Li 2 TiSiO 5 Platelets for Low-Voltage Lithium Battery Anode

Titanium-based anode materials are attracting considerable attention for use in high-performance lithium-ion batteries, but the compromised energy density caused by high voltage plateaus and unsatisfactory capacities severely retards their practical applications. Herein, a molten-salt synthesis of Li 2 TiSiO 5 crystalline platelets and a subsequent selective facet modification by in situ growth of TiO 2 nanocrystal frames are facilely achieved. The discharge voltage plateau at around 0.5 V renders the Li 2 TiSiO 5 anode safe compared with graphite and confers a high energy density compared with zero-strain Li 4 Ti 5 O 12 anode. With the optimized size, structure, and content of modified TiO2 nanocrystals associated with the exposed (001) plane of Li 2 TiSiO 5 , the Li 2 TiSiO 5 -based anodes can deliver a capacity of above 300 mAh g -1 , enhanced rate performance, and a capacity retention of 66% after 10 000 cycles. In situ X-ray diffraction and ex situ transmission electron microscopy have demonstrated the structural stability of the anodes upon charge/discharge. Further theoretical calculation reveals 3D migration paths of Li + ions in Li 2 TiSiO 5 . The selective modification of in situ grown TiO2 nanocrystals on certain facets of crystallites opens a new door for the development of electrode materials possessing superior electrochemical properties.

anodes↗
DOE OSTI2019

Revealing the Atomic Origin of Heterogeneous Li-Ion Diffusion by Probing Na

Tracing the dynamic process of Li-ion transport at the atomic scale is a long-cherished wish in solid state ionics and essential for battery material engineering. Approaches via phase change, strain, and valence states of redox species are developed to circumvent the technical challenge of direct imaging Li, however, all are limited by poor spatial resolution and weak correlation with state-of-charge (SOC). Here, we adopt an ion-exchange approach by sodiating the de-lithiated cathode and probing Na distribution to trace the Li de-intercalation, which enables us to visualize the heterogeneous Li-ion diffusion down to atomic level. In the model LiNi1/3Mn1/3Co1/3O2 cathode, dislocation-mediated ion diffusion is kinetically favorable at low SOC and planar diffusion along (003) layers dominates at high SOC, which work synergistically to determine the ion diffusion dynamics. Our work unveils the heterogeneous nature of ion diffusion in battery material and stresses the role of defect engineering in tailoring ion transport kinetics.

Xiao, Biwei↗
DOE OSTI2021

Vacancy‐Enabled O3 Phase Stabilization for Manganese‐Rich Layered Sodium Cathodes

Abstract Manganese‐rich layered oxide materials hold great potential as low‐cost and high‐capacity cathodes for Na‐ion batteries. However, they usually form a P2 phase and suffer from fast capacity fade. In this work, an O3 phase sodium cathode has been developed out of a Li and Mn‐rich layered material by leveraging the creation of transition metal (TM) and oxygen vacancies and the electrochemical exchange of Na and Li. The Mn‐rich layered cathode material remains primarily O3 phase during sodiation/desodiation and can have a full sodiation capacity of ca. 220 mAh g −1 . It delivers ca. 160 mAh g −1 specific capacity between 2–3.8 V with >86 % retention over 250 cycles. The TM and oxygen vacancies pre‐formed in the sodiated material enables a reversible migration of TMs from the TM layer to the tetrahedral sites in the Na layer upon de‐sodiation and sodiation. The migration creates metastable states, leading to increased kinetic barrier that prohibits a complete O3‐P3 phase transition.

Xiao, Biwei↗
DOE OSTI2025

Developing Digital Twin Visualizations: A Methodology and Case Study on Chemical Separation Processing

As advances in digital engineering continue to push the technological boundaries, digital twin (DT) visualizations for diagnostics and safeguards advancement become much more feasible and practical. DTs generate large and complex data streams that require effective user interfaces to provide monitoring and diagnostic capabilities. Unfortunately, while these frameworks exist, there is not much research on the systematic documentation of human–computer interaction (HCI) for DT visualization. This work presents a dual-mode visualization methodology (two dimensional [2D] graphical user interface dashboard and 3D mixed reality) designed to support diagnostic tasks in DT systems and building on a validated framework and applying established HCI principles. The methodology is demonstrated through a case study of aqueous processing at Idaho National Laboratory, using experimental data from the chemical solvent extraction runs. Our interfaces display real-time alerts and monitoring to inform users of safeguards anomalies. The interfaces use immersive 3D mixed-reality visualization for further system and experiment investigation. This work demonstrates how the systematic application of HCI principles can inform DT visualization design for diagnostic and safeguards applications. While formal user evaluation studies remain as future work, this paper documents the systematic design methodology and demonstrates a proof-of-concept implementation.

3D visualization↗
DOE OSTI2018

Pt/Ferric Hydroxyphosphate: An Effective Catalyst for the Selective Hydrogenation of α,β-Unsaturated Aldehydes (Ketones) into α,β-Unsaturated Alcohols

Four micro (nano)-sized mesoporous ferric hydroxyphosphates (FHP) are synthesized by a reverse microemulsion-solvothermal method, and then are used as supports to prepare supported Pt catalysts. The mean particle diameter of Pt nanoparticles (NPs) was 4.5–4.6 nm. When the four different Pt/FHP catalysts were used into the hydrogenation of α,β-unsaturated aldehydes (ketones) to their corresponding unsaturated alcohols, Pt/FHP (c) catalyst showed better catalytic performance than the other three partners. Under the optimal experimental conditions, several tested α,β-unsaturated aldehydes could be effectively transformed into corresponding unsaturated alcohols over Pt/FHP (c) catalyst. The catalyst could be recycled and reused several times without activity loss. We propose the stronger interaction between the Pt NPs and ferric ions of the FHP (c) are responsible for its good catalytic performance, and this stronger interaction should be rooted in its enhanced Lewis acid strength.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗