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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 OSTI2024

SQMS Quantum R&D in Machine Learning, Optimization and Sensing beyond Fundamental Physics Applications

This newly formed team at SQMS under the Ecosystem Thrust is looking to develop capabilities impacting societal advances outside the core domain of HEP and condensed matter physics. We explicitly leverage the experimental and algorithmic innovations developed across all groups as well as connect to broad-scope external projects of the diverse team of PIs. As the inaugural set of projects, we are studying numerically quantum machine learning models inspired by efficiently trainable echo-state and orthogonal neural networks and developing designs for related experiments to be performed on quantum processors based on SQMS SRF cQED technology and Rigetti s transmon arrays. Investigated models exploit ideas and lessons learned from multiple prior work by SQMS team members in a variety of internal and external activities [R1]. Target initial applications include noisy signal processing, potentially captured by quantum sensors or noisy QPUs, as well as simulation and classification of healthcare data. For instance, image reconstruction of the brain s electrical properties by solving the inverse Maxwell equation problem with uncertainty [R2] through a hybrid quantum-classical physics-informed architecture for time-dependent processes [R3]. The group is also investigating the application and development of novel quantum sensors based on magnetic levitation of a superconducting sphere coupled to a superconducting qubit. This coupling enables high-precision measurements of the position of the sphere, which can be used for sensitive detection of forces, enabling practical applications such as gravimetry for geophysics analysis, or accelerometry for GPS-denied navigation [R4] [R1] Rieffel, Eleanor G., Ata Akbari Asanjan, M. Sohaib Alam, Namit Anand, David E. Bernal Neira, Sophie Block, Lucas T. Brady et al. "Assessing and advancing the potential of quantum computing: A NASA case study." Future Generation Computer Systems (2024). [R2] Yu, X., Serrall s, J.E., Giannakopoulos, I.I., Liu, Z., Daniel, L., Lattanzi, R. and Zhang, Z., 2023. Pifon-ept: Mr-based electrical property tomography using physics-informed fourier networks. IEEE Journal on Multiscale and Multiphysics Computational Techniques. [R3] Wudarski, Filip, Daniel OConnor, Shaun Geaney, Ata Akbari Asanjan, Max Wilson, Elena Strbac, P. Aaron Lott, and Davide Venturelli. "Hybrid quantum-classical reservoir computing for simulating chaotic systems." arXiv preprint arXiv:2311.14105 (2023). [R4] Higgins, Gerard, Saarik Kalia, and Zhen Liu. "Maglev for dark matter: Dark-photon and axion dark matter sensing with levitated superconductors." Physical Review D 109.5 (2024): 055024.

Venturelli, Davide↗
DOE OSTI2024

FORCE Update 2024

The Framework for Optimization of Resources and Economics (FORCE) tool suite is the U.S. Department of Energy’s Nuclear Integrated Energy Systems (IES) Program flagship tool suite for technoeconomic IES analysis of IES. This tool suite is useful for analysis designed to evaluate and improve the technoeconomics of energy production systems, particularly for systems including nuclear technology. In this report, we document the development activity for the FORCE tool suite to extend its capabilities as performed during fiscal year 2024. In addition to reliability and accessibility, capability is one of the three standards guiding the development of the FORCE tool suite and the software codes that are its constituent parts. Extending the capabilities of the FORCE tool suite allows analysis both within the IES program as well as industry, university, and laboratory partners to perform analysis with more accuracy, insight, and impactful narrative. Four areas of capability development were the focus of activity this year: economic parameter uncertainty quantification, multiresolution analysis, components-to-optimization workflow automation, and statespace construction workflows for real-time optimal control. In economic parameter uncertainty quantification, the ability of HERON to capture risk due to scenarios (weather and energy demand uncertainty) was expanded to also include uncertainties in financial parameters such as capital cost or operation and maintenance costs. By including these sources of uncertainty, which are sometimes very large compared with scenario uncertainty, HERON is better able to capture the risk posed by investment in various IES technology. Because of this, analysts can also consider the reduction in risks that can be realized by choice of some technologies. In multiresolution analysis, development activity extended on work completed previously. In fiscal year 2023, methods for decomposing time series signals, such as demand, solar and wind availability, and price profiles, were analyzed and down-selected to those most effective at splitting signals into different resolutions. These resolutions allow considering the influence of different energy demand and supply behaviors across different time scales. For example, energy demand might be divided into seasonal, weekly, and hourly profiles. In fiscal year 2024, this preliminary work was extended and implemented within the Risk Analysis Virtual Environment (RAVEN) risk and uncertainty analysis platform, which is used throughout the FORCE framework. This development of the “multi-resolution time series analysis” (MR-TSA) module in RAVEN allows training synthetic history generators on complex time series. These synthetic history generators can then be used in HERON for generating scenarios that represent possible market and weather scenarios that can be analyzed on different time scales. We envision completing this work in the future, implementing multiresolution dispatch optimization strategies that can make the most beneficial use of these stratified time histories. In components-to-optimization workflow development, workflows for translating user inputs of components into algorithms for algebraic optimization were selected and implemented. Similar algorithms within the Holistic Energy Resource Optimization Network (HERON) were separated from the main code base of HERON and gathered with the components-to-optimization workflows in the new Dispatch Optimization Variable Engine (DOVE) software library. This modularization allows FORCE users to analyze dispatch optimization and energy system duty cycles independently of HERON, which previously was a burdensome task. Additionally, these dispatch optimization algorithms, set up in an independent library, can now be used across all software applications within FORCE, especially including the real-time optimal control software Optimization of Real-time Capacity Allocation (ORCA). Allowing FORCE software to share dispatch optimization algorithms within a single library allows for improved software maintenance and reliability. In statespace characterization workflow development, alternative workflows for optimizing dispatch with additional technical accuracy was the focus, particularly to improve the real-time optimization decision making in ORCA. Using algorithms and workflows initially developed for the Feasible Actuator Range Modifier (FARM), workflows for determining the statespace representation of IES were identified and demonstrated. The resulting dispatch optimization required a more robust optimization algorithm than that originally used in HERON (and moved to DOVE), which required adding an alternate workflow to DOVE that can more accurately match the behavior of physical systems using a partial differential equation representation. In conclusion, capability developments in the FORCE tool suite in fiscal year 2024 have improved the ability of the FORCE tool suite to perform

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Enterprise Artificial Intelligence Strategy for Los Alamos National Laboratory

In the 1984 martial arts drama film, The Karate Kid, a young Daniel LaRusso is unexpectedly placed in an adversarial environment unable to eYectively adapt to a series of new threats and limitations. Fortunately for the main character, once placed under the tutelage of a Mr. Miyagi, he finds resiliency not through the adoption of new tools, but a re-focused set of fundamentals. Much in the same way that Daniel learns waxing on and buYing oY car wax by hand has rewards for Karate, LANL is choosing the harder path of self-hosting Large Language Models (LLMs) for enterprise use instead of only relying on buying access to a hosted AI service like Azure’s OpenAI Application Programming Interface (API). We also are not willing to wait for software-as-a-service (SAAS) AI services to meet us where we need to be from a FedRAMP accreditation standpoint. Our operations regularly depend on access at CUI, UCNI, ITAR and other FIPS-199 moderate-impact data levels and hosting our own services gives us the right security and compliance posture to be useful across the broad range of our work at LANL. With the rise in threats to critical infrastructure, cloud service providers (CSPs), and supply chain attacks from both state and non-state actors, we are not placing the bet that SAAS hosted AI services will be available when we need them. Should a major event occur, we do not want our staY and operations left without a pathway for us to fix the problem and resume the use of AI tools.

42 ENGINEERING↗
DOE OSTI2025

Analysis of runaway electron driven whistler wave instability experiments

Data acquired on the DIII-D tokamak were analyzed. The data are from experiments that were conducted to study an instability that is driven unstable by intense populations of electrons with MeV energies that are known as runaway electrons. The instability is a type of plasma wave called a whistler wave that occurs at frequencies above the ion cyclotron frequency but well below the electron cyclotron frequency. The waves were measured by magnetic fluctuation coils that are embedded in the DIII-D vacuum vessel wall. After upgrades to this diagnostic were completed, new experiments were conducted on July 13, 2020 in order to measure the toroidal mode number of the whistler waves and to extend the frequency of the detected waves. Through the use of mixers, instability between 600-700 MHz was detected. (The initial experiments only measured up to 200 MHz.) Analysis of the data was led by Hari Choudhury, a PhD student at Columbia University. Mr. Choudhury has submitted two papers for publication that include contributions by UC Irvine (UCI) Professor Heidbrink and his graduated PhD student Genevieve DeGrandchamp: “Detailed Characterization of Runaway Electron Driven Whistler Waves in Low-Density DIII-D Discharges” and “First Demonstration of Resonant Pitch-Angle Scattering of Relativistic Electrons by Externally-Launched Helicon Waves.” The first paper, which has been submitted to Physics of Plasmas, has significant contributions to both the data and the interpretation by UCI scientists. In contrast, UCI contributions to the second paper, which has been submitted to Physical Review Letters, are relatively minor.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Medium Energy Nuclear Physics Research at the University of Richmond

The first part is a technical paper on the fitting techniques used to extract the neutron detec tion efficiency from the CLAS12 detector at the Thomas Jefferson National Accelerator Facility in Newport News, VA. The second is the masters thesis of Mr. Jude Buckley who also studied the neutron detection efficiency. He was a student at the University of Surrey in the UK and supported by the grant.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Pump Development for Fusion-Based Neutron Generators

Tritium usage in fusion-based neutron generators is low for each pulse. This requires the deuterium/tritium mixture to be evacuated and recycled during downtime or to remove the helium buildup. Because of the pressure ranges and to minimize space, turbomolecular pumps are being considered for the tritium recirculation pumps. Two main types were initially considered: Agilent TwisTorr and Osaka TG-MR.

Angelette, Lucas M. [Savannah River National Labor↗
DOE OSTI2024

Circulating levels of micronutrients and risk of osteomyelitis: a Mendelian randomization study

Background Few observational studies have investigated the effect of micronutrients on osteomyelitis, and these findings are limited by confounding and conflicting results. Therefore, we conducted Mendelian randomization (MR) analyses to evaluate the association between blood levels of eight micronutrients (copper, selenium, zinc, vitamin B12, vitamin C, and vitamin D, vitamin B6, vitamin E) and the risk of osteomyelitis. Methods We performed the two-sample and multivariable Mendelian randomization (MVMR) to investigate causation, where instrument variables for the predictor (micronutrients) were derived from the summary data of micronutrients from independent cohorts of European ancestry. The outcome instrumental variables were used from the summary data of European-ancestry individuals ( n = 486,484). The threshold of statistical significance was set at p < 0.00625. Results We found a significant causal association that elevated zinc heightens the risk of developing osteomyelitis in European ancestry individuals OR = 1.23 [95% confidence interval (CI) [1.07, 1.43]; p = 4.26E-03]. Similarly, vitamin B6 showed a similar significant causal effect on osteomyelitis as a risk factor OR = 2.78 (95% CI [1.34, 5.76]; p = 6.04E-03; in the secondary analysis). Post-hoc analysis suggested this result (vitamin B6). However, the multivariable Mendelian randomization (MVMR) provides evidence against the causal association between zinc and osteomyelitis OR = 0.98(95% CI [−0.11, 0.07]; p = 7.20E-1). After searching in PhenoScanner, no SNP with confounding factors was found in the analysis of vitamin B6. There was no evidence of a reverse causal impact of osteomyelitis on zinc and vitamin B6. Conclusion This study supported a strong causal association between vitamin B6 and osteomyelitis while reporting a dubious causal association between zinc and osteomyelitis.

Zhang, Xu↗
DOE OSTI2025

Testing convolutional neural network based deep learning systems: a statistical metamorphic approach

Machine learning technology spans many areas and today plays a significant role in addressing a wide range of problems in critical domains,i.e., healthcare, autonomous driving, finance, manufacturing, cybersecurity,etc. Metamorphic testing (MT) is considered a simple but very powerful approach in testing such computationally complex systems for which either an oracle is not available or is available but difficult to apply. Conventional metamorphic testing techniques have certain limitations in verifying deep learning-based models (i.e., convolutional neural networks (CNNs)) that have a stochastic nature (because of randomly initializing the network weights) in their training. In this article, we attempt to address this problem by using a statistical metamorphic testing (SMT) technique that does not require software testers to worry about fixing the random seeds (to get deterministic results) to verify the metamorphic relations (MRs). We propose seven MRs combined with different statistical methods to statistically verify whether the program under test adheres to the relation(s) specified in the MR(s). We further use mutation testing techniques to show the usefulness of the proposed approach in the healthcare space and test two CNN-based deep learning models (used for pneumonia detection among patients). The empirical results show that our proposed approach uncovers 85.71% of the implementation faults in the classifiers under test (CUT). Furthermore, we also propose an MRs minimization algorithm for the CUT, thus saving computational costs and organizational testing resources.

Computer Science↗
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 OSTI2026

Application of an Empirical Density Law via Python for Aqueous Plutonium Chloride Systems for MCNP6

Criticality safety models for aqueous plutonium chloride systems often contain a significant bias due to assumptions in material compositions. These systems are currently modeled as a fictitious metal-water mixture because little is known about the true solution density. Furthermore, no predictive density tools or capabilities for modeling aqueous plutonium chloride systems are approved for use at Los Alamos National Laboratory. Recent density measurements of this ternary system (PuCl 3 -HCl-H 2 O) have allowed for the development of a more realistic density law, which is applied in this work via an empirical method based in Python. This tool, entitled PuCS (Plutonium Chloride Solution tool) may be used to determine solution density and composition based on the plutonium content, acid content, and temperature for MCNP6 inputs. PuCS has been found to predict density within 2% of experimental data. In conclusion, MCNP6 calculations have found that crediting minimal amounts of free acid (0.5 M) may correspond to a ~12% decrease in peak reactivity in comparison to current modeling methods.

42 ENGINEERING↗
DOE OSTI2026

CRGTBSO3 TBS Ozone Data

The Tethered Balloon System (TBS) operated for two weeks during a summer IOP of the CoURAGE campaign. This dataset includes data collected from an En-Sci electrochemical cell (ECC) ozonesonde on the TBS. The ozonesonde was connected to an iMet-4RSB radiosonde, and the overall data collected included ozone, relative humidity, temperature, and altitude. The data from the iMet is the same as that found in the TBSMERGED data product. The TBS also had another instrument (iMet XQ2) that collected meteorological data, which may have more accurate relative humidity (RH) data. This ozonesonde data set is intended to complement the TBSMERGED data product, the CRGTBSO3 surface ozone measurements, and the CoURAGE SWARM ozone lidar (TOLNet) measurements from other locations.

Atmospheric relative humidity↗
DOE OSTI2025

Beartooth - Digital Twin Framework Enabling AI

Digital twin was designed as a core part of this testbed. This presentation will discuss the digital twin framework that will enable AI for nuclear aqueous seperations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗