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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 sources NASA NTRS DOE OSTI

DOE OSTI2021

LOXG473A induces the formation of osteoclasts in RAW264.7 cells via IL-6/JAK2/STAT3 signaling

Formation of osteoclasts is known to be closely associated with osteoporosis progression. LOX is a key enzyme that catalyzes the synthesis of collagen, which is the new mediator in osteoclast formation. However, the effect of LOXG473A on of osteoclast formation needs to be explored. Thereby, we sought to explore the effect of LOX{sub G473A} on formation of osteoclasts and its underlying mechanism. To investigate the function of LOX{sub G473A} in osteoclast formation, Raw264.7 cells were stably transfected with LOX-WT or LOX-MUT (LOX{sub G473A}). Real-time PCR and western blotting were used to detect the relative levels of osteoclast formation related genes and proteins. TRAP staining and immunofluorescence staining were used to test the ability of Raw264.7 cells to form osteoclasts and the ability of cells to form rings, respectively. Bone erosion assay was used to test bone resorptive activity. The data indicated that LOX{sub G473A} significantly enhanced the ability of osteoclasts forming, ring-forming and bone resorpting in Raw264.7 cells. Mechanically, LOX{sub G473A} upregulated the expressions of NFATC1, ACP5, CTSK, IL-6, and the proportion of p-JAK2/JAK2 and p-STAT3/STAT3, thereby promoting the formation of osteoclasts. In conclusion, we have verified that LOX{sub G473A} induces the proliferation of osteoclasts in Raw264.7 cells via IL-6/JAK2/STAT3 signaling, suggesting a novel strategy for studying osteoporosis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗

DOE OSTI2025

D–MOPH–25: diverse MOF–molecule pairs for Henry’s constants prediction

Computational methods like grand-canonical Monte Carlo simulations and machine learning (ML) have accelerated metal–organic frameworks (MOF) exploration but are typically limited to a narrow range of adsorbates due to data availability and force field constraints. In this study, we introduce a dataset of diverse MOF–molecule pairs for Henry’s constant prediction, D–MOPH–25, which systematically explores a diverse chemical space by combining 113 molecular adsorbates with over 5000 MOF structures through an active learning process. D–MOPH–25 constitutes the most diverse adsorbate dataset used in any ML study of molecular adsorption in MOFs to date. Our workflow builds a benchmark for predicting Henry’s constants at 300 K, leveraging conformal prediction for uncertainty quantification. Assessment through Shannon entropy and uniform manifold approximation and projection confirms the comprehensiveness of D–MOPH–25 while highlighting the importance of robust classification to filter out unphysical data points in regression tasks. Although future enhancements in model architecture and sampling criteria could improve predictive performance, our dataset already spans the target space using only 2.31% of total possibilities. This comprehensive dataset facilitates assessment of model generalizability across adsorbate species and can establish a foundation for high-throughput MOF screening and ML-driven separation processes.

active learning↗

DOE OSTI2024

Deep Learning-enhanced Block-Diagram Modeling of Solar Power Systems

Data-driven models of power system inverter-based resources are desired to run simulations faster than with detailed electromagnetic transient models, to hide proprietary design details, to support control system design applications, and to aggregate the effects of distributed energy resources. This paper applies a customized Hammerstein Wiener framework to train block diagram models from thousands of electromagnetic transient simulations or experimental test records. The block diagram models integrate with larger grid simulations as voltagecontrolled current sources or current-controlled voltage sources for several simulators. Guidelines for block architecture and training are presented. Three-phase balanced, three-phase unbalanced, and single-phase examples all achieve an acceptable root mean square error of no more than 0.05 per-unit.

Mcdermott, Thomas E. [Private consulting company]↗

DOE OSTI2021

Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The data streams are captured using time-synchronized high sampling rates synchro waveform measurement units (SWMU). The proposed method is formulated based on a machine learning method, the convolutional neural network (CNN). This method is capable of capturing the spatiotemporal feature of the measurements effectively and perform the event cause analysis. Several events are considered in this paper to encompass a range of possible events in real distribution networks, including capacitor bank switching, transformer energization, fault, and high impedance fault (HIF). The dataset for our study is generated using the real time digital simulator (RTDS) to simulate real-world events. The event cause analysis is performed using only one cycle of the voltage waveforms after the event is detected. The simulation results show the effectiveness of the proposed machine learning-based method compared to the state-of-the-art classifiers.

Niazazari, Iman↗

DOE OSTI2024

Federated Deep Reinforcement Learning for Decentralized VVO of BTM DERs

The future of grid control requires a hybrid approach combining centralized and decentralized methods to fully utilize the potential of smart edge devices with artificial intelligence (AI) capabilities. This paper aims to develop and evaluate a federated deep reinforcement learning (FDRL) framework for decentralized adaptive volt-var optimization (VVO) of behind-the-meter (BTM) distributed energy resources (DERs). First, this paper models a single deep reinforcement learning (DRL) agent using the Markov Decision Process (MDP) framework for decentralized adaptive VVO of BTM DERs. Two DRL algorithms, soft actor-critic (SAC) and twin-delayed deep deterministic policy gradient (TD3), are compared for their effectiveness in optimizing VVO. Results show that TD3 outperforms SAC, achieving a 71.3% improvement in mean reward. Finally, the DRL agent is deployed within the FDRL framework, using the Flower platform, to enhance learning, provide adaptive control, and ensure data privacy for BTM DERs.

Ravi, Abhijith↗

DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗

DOE OSTI2023

Enhanced_STIX_Elevator

To more effectively and completely convert STIX1 data to STIX2.

Yue, Meng↗

DOE OSTI2025

TransPlatformer

We propose TransPlatformer for translating toxicogenomics from one platform to another. Transcriptomic profiling has evolved through multiple generations of technology, from microarrays (e.g., Affymetrix, CodeLink) to more recent high-throughput sequencing and targeted panels such as S1500+. Microarrays, which dominated gene expression studies in the early 2000s, provided affordable and high-throughput transcript quantification but suffered from cross-hybridization issues and limited dynamic range . RNA-Seq, introduced in the late 2000s, revolutionized transcriptomics by enabling unbiased and comprehensive gene expression analysis, albeit at higher costs and computational demands . Despite advances, many studies rely on historical microarray data, necessitating the translation of legacy data into modern platforms to ensure continuity and comparability. This translation is complicated by factors such as platform-specific probe design, differences in transcript coverage, and batch effects . Existing methods for cross-platform mapping include statistical normalization, machine learning models, and biological anchoring approaches. The ability to translate transcriptomic data between platforms has broad implications, including enhanced meta-analyses, improved toxicological modeling, and better integration of historical datasets with contemporary research. TransPlatformer seeks to contribute to this effort by evaluating translation methodologies and proposing novel strategies to improve cross-platform gene expression harmonization. In this repository there are code examples for TransPlatformer implementation

Cong, Guojing↗

DOE OSTI2021

Effect of High Temperature CO 2 on Haynes 230 Alloy (Updated Jan 2021)

The supercritical carbon dioxide (sCO 2 ) Brayton cycle is a promising candidate for future nuclear reactors due to its ability to improve power cycle energy conversion efficiency. The sCO 2 Brayton cycle can operate with an efficiency of 45-50% at operating temperatures of 550-700 C. One of the greatest hurdles currently faced by sCO 2 Brayton cycles is the corrosivity of sCO 2 and the lack of long-term alloy corrosion and mechanical performance data, as these will be key to enhancing the longevity of the system, and thus the levelized cost of electricity. Past studies have shown that sCO 2 corrosion occurs through the formation of metal carbonates, oxide layers, and carburization, and alloys with Cr, Mo and Ni generally exhibit less corrosion. While stainless steels may offer sufficient corrosion resistance at the lower range of temperatures seen by the sCO 2 Brayton cycles, more expensive nickel-based alloys are typically needed for the higher temperature regions. This study investigates the effects of corrosion on the Haynes 230 alloy, with a preliminary view on changes in the mechanical properties. High temperature CO 2 is used for this study as the corrosion products are similar to that of supercritical CO 2 , allowing for an estimation of the susceptibility towards corrosion without the need for high pressure experimentation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Strategic Petroleum Reserve Enhanced Monitoring Compendium - FY 2021

The Strategic Petroleum (SPR) is the world’s largest supply of crude oil. The reserve consists of fours sites in Louisiana and Texas. Each site stores crude in deep, underground salt caverns. It is the mission of the SPR’s Enhanced Monitoring Program to examine all available data to inform our understanding of each site. This report discusses the data, processes and results for each of the four sites.

02 PETROLEUM↗

DOE OSTI2022

A study of sacrificial mirrors for use prior to a laser wakefield accelerator driven by the Z-Petawatt laser

Many experiments at Sandia’s Z Pulsed Power Facility require x-ray backlighting diagnostics to understand experiment performance. Due to limitations in present-day source/detection modalities, most x-ray diagnostics at Z are restricted to photon energies <20 keV, ultimately limiting the density, amount, and atomic number of targets diagnosable in experiments. These limitations force the use of low-Z materials like Beryllium, and they prevent acquisition of important backlighting data for materials/densities that are opaque to soft x-rays and where background emission from the Z load and transmission lines overwhelm diagnostics. In this LDRD project, we have investigated the design and development of a laser wakefield acceleration platform driven by the Z-Petawatt laser – a platform that would enable the generation of a pulsed, collimated beam of high energy x-rays up to 100 keV. Geometrical considerations for implementation on the Z Machine require the use of sacrificial mirrors, which have been tested in offline experiments in the Chama target chamber in building 983. Our results suggest the use of sacrificial mirrors would not necessarily inhibit the laser wakefield x-ray process, particularly with the benefits stemming from planned laser upgrades. These conclusions support the continuation of laser wakefield source research and the development of the necessary infrastructure to deliver the Z-Petawatt laser to the Z center section along the appropriate lines of sight. Ultimately, this new capability will provide unprecedented views through dense states of matter, enabling the use of previously incompatible target materials/designs, and uncovering a new set of observables accessible through diffraction and spectroscopy in the hard x-ray regime. These will amplify the data return on precious Z shots and enhance Sandia’s ability to investigate fundamental physics in support of national security.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

FY22 Status Report on the ART-GCR CMVB and CNWG International Collaborations

This work presents the numerical model for the high temperature test reactor (HTTR) loss of forced cooling (LOFC) experiment with the INL codes Griffin, BISON, and RELAP-7, based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Promising results were obtained, with the overall behavior of the reactor successfully captured. Changes in the heat transfer coupling, as compared to the fiscal year (FY)-21 model, enabled drastic improvement of the steady-state solution, both in terms of computational time and global energy discrepancies. The former was reduced by a factor of roughly 60, whereas the latter decreased from 11% to less than 2%. Furthermore, the discrepancy in the steady-state multiplication factor was improved from +2,300 and +2,900 pcm (for the 30 and 9 MW cases) to -700 and +1,200 pcm, respectively, and now falls well within the large measurement uncertainties stemming from graphite impurities. Validation of the Monte Carlo model used to generate cross sections was also performed against available measurements. Though significant, the discrepancies remain acceptable overall in light of the large uncertainty. Numerous improvements are still needed to better compare with the experiment involving the 9 MW case and to instill greater confidence in the model’s ability to accurately predict the 30 MW behavior. Specifically, the power levels predicted by the 9 MW transient simulation following re-criticality remain low, pointing to an underestimation of the passive cooling of the core. A key aspect of future work will be to better understand the flow pattern during the LOFC event, particularly to determine if natural or forced convection is occurring inside the reactor pressure vessel (RPV). More generally, additional validation data would be immensely useful for further enhancing the numerical model and better matching the experiments. In addition, a more sophisticated thermal-hydraulics model that simulates all the channels as a single system model should be considered to take into account the rest of the primary loop. Finally, even if the results are in better agreement with the experiments, sensitivity analysis and uncertainty quantification will be necessary to evaluate the model uncertainty.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Strategic Petroleum Reserve Enhanced Monitoring Compendium (FY 2022)

The Strategic Petroleum Reserve (SPR) is the world's largest supply of emergency crude oil. The reserve consists of four sites in Louisiana and Texas. Each site stores crude in deep, underground salt caverns. It is the mission of the SPR's Enhanced Monitoring Program to examine all available data to inform our understanding of each site. This report discusses the monitoring data, processes, and results for each of the four sites for fiscal year 2022.

02 PETROLEUM↗

DOE OSTI2023

Talent Acquisition in Scientific and Engineering Disciplines: A Case Study of National Laboratory (TASED) [Thesis]

Identifying the hindrances to performing effective talent acquisition within the science, technology, engineering, and mathematics field is an important topic for technical hiring managers. Top candidates have multiple options during highly competitive market conditions requiring managers to look for unique solutions which diverge from competition. Prior to this study there has been very little research considering national laboratory research and development challenges from a technical hiring manager’s talent acquisition perspective. Utilizing a unique combination of national laboratory multi-organization survey, pilot study, Human Resource (HR) tracking data and trust based business strategy to enhance partnering this research finds hiring managers can leverage out of the box techniques to improve internal processes while developing industry support to target highly qualified individuals. This methodology could be utilized by technical hiring managers across federal national laboratory enterprise to effectively capture next generation staff and leadership talent who align with their organization professionally as well as social culture.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Strategic Petroleum Reserve Enhanced Monitoring Compendium (FY23)

The Strategic Petroleum Reserve (SPR) is the world’s largest supply of emergency crude oil. The reserve consists of four sites in Louisiana and Texas. Each site stores crude in deep, underground salt caverns. It is the mission of the SPR’s Enhanced Monitoring Program to examine available sensing data to inform our understanding of each site. This report discusses the monitoring data, processes, and results for each of the four sites for fiscal year 2023.

02 PETROLEUM↗

DOE OSTI2024

Strategic Petroleum Reserve Enhanced Monitoring Compendium - FY24

02 PETROLEUM↗

DOE OSTI2026

Grid Topology Discovery Algorithm Evaluation of Suitability for Utility Deployment (CRADA 606 Final Report)

This work presents the results of a field-informed demonstration aimed at evaluating the practical suitability of a topology discovery algorithm for utility environments. We demonstrated an algorithm that uses a graph-theory-informed state estimation approach for model selection. In collaboration with Survalent and Peninsula Light Co., the algorithm was applied to real feeder models and field measurements from supervisory control and data acquisition (SCADA) and advanced metering infrastructure (AMI) systems to identify the operational topology of a power distribution system. The demonstration assessed the algorithm’s performance under realistic data conditions, including sparse and noisy measurements, and examined its ability to identify the most likely network configurations. The results confirmed that the approach can effectively narrow down feasible topologies, providing operators with improved situational awareness of network status. Key lessons learned emphasize the need for systematic data validation and strategic sensor placement to enhance observability. These insights inform future deployment strategies and guide refinements for broader adoption in utility operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗