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

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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Investigating Resilience of Loops in HPC Programs: A Semantic Approach with LLMs

Soft errors have become one of the major concerns for the error resilience of the HPC applications as those errors may cause HPC applications to generate serious outcomes such as silent data corruptions (SDCs). Protecting the applications from soft errors is an essential while challenging task. Among different approaches, obtaining a profound understanding of the resilience proneness of an application is very important to devise efficient error detection and recovery strategies. Given the scale of the HPC applications both in the code size and execution time, there are often cases that the error propagation analysis on such applications would produce a massive volume of unstructured data, which requires a significant amount of efforts, to process and to obtain indicating actions towards error protection. In this paper, we present a control-flow based visual analysis framework to help the users conduct error propagation analysis and identify the critical sections of a program that may have a higher likelihood of leading to erroneous outcomes when affected by the control flow related errors. We also design and implement the scalable visualization framework - ResilienceVis that efficiently and effectively visualizes the affected program states under errors and the propagation traces for an application in a user-friendly manner, and eventually, we combine the analysis and visualization to exhibit the error-proneness of the different sections of applications.

Jiang, Hailong↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2026

Oxide nucleation via threshold-driven volume instability in Ni oxidation

Oxide nucleation dictates the onset of metal oxidation, yet the atomistic pathways bridging initial oxygen adsorption to bulk phase transformation remain elusive. Here, we utilize in-situ atomic-resolution imaging to directly capture the nucleation of nickel oxide, revealing a threshold-driven, cooperative transformation of Ni into NiO. Our observations identify a distinct incubation period during which oxygen progressively accumulates in subsurface layers. Once a critical concentration and penetration depth are reached, a collective lattice reconfiguration is triggered, abruptly converting multiple Ni layers into NiO. Atomistic simulations corroborate this mechanism, identifying a cooperative lattice instability induced by subsurface oxygen saturation. These results establish subsurface oxygen incorporation as the missing mechanistic link between surface adsorption and three-dimensional oxide formation, providing an atomistic framework to understand and control reactive phase transformations in materials synthesis, catalysis, and degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A framework for understanding efficient diurnal CO 2 reduction using Si and GaAs photocathodes

Integrated solar fuels and photoelectrochemical (PEC) CO 2 reduction (CO 2 R) are promising pathways toward producing value-added chemicals from CO 2 . However, improvements are needed in activity and selectivity as well as in fundamental understanding of device behavior to engender wide deployment. Here, we report two single-junction, integrated photocathodes for PEC CO 2 R based on TOPCon Si and GaAs substrates, which achieve -10 mA cm -2 at -0.33 V vs. RHE with 41% selectivity to C 2+ products and at -0.03 V vs. RHE with 27% selectivity to C 2+ products, respectively. We investigated the viability of a light-mediated strategy to direct selectivity in buried-junction PEC devices and confirmed that these devices could be optimized independently and described by the physics-based models of the individual components. Finally, we designed a framework to assess operational modes for PEC CO 2 R devices and demonstrated this framework under continuous galvanostatic control and variable illumination conditions.

14 SOLAR ENERGY↗
DOE OSTI2025

Universal Magnetic Phases in Twisted Bilayer MoTe 2

Twisted bilayer MoTe 2 (tMoTe 2 ) has emerged as a robust platform for exploring correlated topological phases, yet the evolution of its magnetism and topology with twist angle remains an open question. Here, we systematically map the magnetic phase diagram of tMoTe 2 by using local optical spectroscopy and scanning nanoSQUID-on-tip magnetometry. We identify spontaneous ferromagnetism at filling factors ν = −1 and −3 across twist angles from 2.1° to 3.7°, revealing a universal, twist-angle-insensitive ferromagnetic phase. At 2.1°, we further observe robust ferromagnetism at ν = −5, absent at larger twist angles. Temperature-dependent measurements reveal a contrasting twist-angle dependence of the Curie temperatures between ν = −1 and −3, indicating a distinct interplay between the exchange interactions and bandwidth for the two Chern bands. Despite broken time-reversal symmetry, no topological gap is detected at ν = −3. Furthermore, our results establish a global framework for understanding and controlling magnetic order in tMoTe 2 .

Insulators↗
DOE OSTI2022

Direct Observation of Ammonia Storage in UiO-66 Incorporating Cu(II) Binding Sites

The presence of active sites in metal–organic framework (MOF) materials can control and affect their performance significantly in adsorption and catalysis. However, revealing the interactions between the substrate and active sites in MOFs at atomic precision remains a challenging task. Here, we report the direct observation of binding of NH 3 in a series of UiO-66 materials containing atomically dispersed defects and open Cu(I) and Cu(II) sites. While all MOFs in this series exhibit similar surface areas (1111–1135 m 2 g –1 ), decoration of the -OH site in UiO-66-defect with Cu(II) results in a 43% enhancement of the isothermal uptake of NH 3 at 273 K and 1.0 bar from 11.8 in UiO-66-defect to 16.9 mmol g –1 in UiO-66-Cu II . A 100% enhancement of dynamic adsorption of NH 3 at a concentration level of 630 ppm from 2.07 mmol g –1 in UiO-66-defect to 4.15 mmol g –1 in UiO-66-Cu II at 298 K is observed. In situ neutron powder diffraction, inelastic neutron scattering, and electron paramagnetic resonance, solid-state nuclear magnetic resonance, and infrared spectroscopies, coupled with modeling reveal that the enhanced NH 3 uptake in UiO-66-Cu II originates from a {Cu(II)···NH 3 } interaction, with a reversible change in geometry at Cu(II) from near-linear to trigonal coordination. This work represents the first example of structural elucidation of NH 3 binding in MOFs containing open metal sites and will inform the design of new efficient MOF sorbents by targeted control of active sites for NH 3 capture and storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Toward Unified Autonomous Scattering Experiments: A Cross-Facility Case Study at ALS and PETRA III

Autonomous experiments rely on the integration of control, data acquisition, analysis, and decision-making frameworks. While such systems have been demonstrated at individual facilities, adapting them to additional instruments remains challenging due to differences in local infrastructure. We present a modular workflow that connects existing open-source tools for data access (Tiled), workflow orchestration (Prefect), analysis and visualization (pyFAI, Plotly Dash), and Gaussian-process-based adaptive sampling (gpCAM) into a unified framework for autonomous scattering experiments. The same configuration operates across two synchrotron beamlines (ALS 7.3.3 and PETRA III P03) with only minimal facility-specific adjustments, as shown in proof-of-concept demonstrations. This validates that a consistent design emphasizing modularity and shared interfaces can ease deployment across diverse experimental environments. The resulting framework provides a flexible foundation for extending autonomous control and analysis capabilities beyond a single beamline or instrument.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Modulation of crystal growth and structure within cerium-based metal–organic frameworks

The intriguing catalytic properties of cerium-based materials have motivated the development of Ce-based metal–organic frameworks (MOFs). However, the controlled crystallization of Ce MOFs remains nascent due to complications with the high reduction potential of Ce 4+ species. Modulators offer a route in other well-studied coordination networks to slow down crystallization processes to allow for corrective, uniform crystal growth. Herein, we report an investigation of modulator identity and concentration on the synthesis of a Ce-UiO-type MOF with 2,6-naphthalenedicarboxylic acid (NDC) as a linker. At low concentrations using both benzoic acid and trifluoroacetic acid, we observed a mononuclear Ce 3+ MOF (NU-350) through single-crystal X-ray diffraction studies. Higher modulator concentrations yielded pure-phase Ce-UiO-NDC, with uniform particle sizes observed with utilizing benzoic acid as a modulator. Moreover, we demonstrated the transferability of this synthesis through the pure-phase synthesis of Ce-UiO-66 with benzoic acid as a modulator. High-resolution transmission electron microscopy showed a single crystalline domain within Ce-UiO-NDC.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Elucidating interprotein energy transfer dynamics within the antenna network from purple bacteria

In photosynthesis, absorbed light energy transfers through a network of antenna proteins with near-unity quantum efficiency to reach the reaction center, which initiates the downstream biochemical reactions. While the energy transfer dynamics within individual antenna proteins have been extensively studied over the past decades, the dynamics between the proteins are poorly understood due to the heterogeneous organization of the network. Previously reported timescales averaged over such heterogeneity, obscuring individual interprotein energy transfer steps. Here, we isolated and interrogated interprotein energy transfer by embedding two variants of the primary antenna protein from purple bacteria, light-harvesting complex 2 (LH2), together into a near-native membrane disc, known as a nanodisc. We integrated ultrafast transient absorption spectroscopy, quantum dynamics simulations, and cryogenic electron microscopy to determine interprotein energy transfer timescales. By varying the diameter of the nanodiscs, we replicated a range of distances between the proteins. The closest distance possible between neighboring LH2, which is the most common in native membranes, is 25 Å and resulted in a timescale of 5.7 ps. Larger distances of 28 to 31 Å resulted in timescales of 10 to 14 ps. Corresponding simulations showed that the fast energy transfer steps between closely spaced LH2 increase transport distances by ∼15%. Overall, our results introduce a framework for well-controlled studies of interprotein energy transfer dynamics and suggest that protein pairs serve as the primary pathway for the efficient transport of solar energy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Overview of FLORIS updates

In this paper, we review updates made to the FLORIS (FLOw Redirection and Induction in Steady State) open-source framework for wind farm control modeling, design and analysis. The updates are focused on improvements for large arrays of turbines - including heterogenous inflows and incorporating second-order wake steering effects - design, and analysis tools for practical application of wind farm control, and improvements to the software architecture for best practices in modularization and cooperative development.

17 WIND ENERGY↗
DOE OSTI2022

Design of the American Wake Experiment (AWAKEN) field campaign

The American WAKE experimeNt (AWAKEN) is a multi-institutional collaborative field campaign, starting in March 2022, that will gather an unprecedented data set including both atmospheric observations and wind plant operational data. This comprehensive data set will be used to characterize the wind plant performance and turbine loading in different operational and atmospheric conditions and validate the use of different wind plant control strategies and simulation frameworks. An extensive field campaign like AWAKEN requires proper coordination and long-term planning to be successful. In this paper, we review the major activities planned during AWAKEN to provide information for current and future project partners. Specifically, we provide information about the project sites, their planned instruments, and how these will relate to the scientific objectives of the overall AWAKEN project.

17 WIND ENERGY↗
DOE OSTI2026

Environment-imposed selection rules for nuclear-spin conversion of H 2 in molecular crystals

Nuclear-spin conversion in molecular hydrogen is governed by strict symmetry rules that typically require magnetic fields or catalytic surfaces to break. Here we demonstrate that the intrinsic tensor composition of a non-magnetic molecular crystal field can impose and relax these rules without external fields. High-resolution infrared spectra of H 2 in crystalline CO 2 reveal large rank-2 (quadrupolar) crystal-field splittings of the 𝑚 sublevels, while nuclear-spin conversion occurs only through 𝛥𝑚 = 0 channels. Replacing CO 2 with polar N 2 O introduces rank-1 (dipole) components that partially open 𝛥⁢𝑚 ≠ 0 pathways, while incorporation of paramagnetic NO 2 fully lifts the restriction. Furthermore, these results establish a direct correspondence between crystal-field tensor rank and nuclear-spin dynamics, introducing a general symmetry-based framework for designing and controlling spin-isomer populations and quantum-state connectivity in molecular solids.

McLane, Nathan [University of Maryland, College Pa↗
DOE OSTI2022

Neural Lyapunov Differentiable Predictive Control

We present a learning-based predictive control methodology using the differentiable programming framework with sufficient Lyapunov-based guarantees. The neural Lyapunov differentiable predictive control (NLDPC) learns the policy by constructing a computational graph encompassing the system dynamics, state, and input constraints, and the necessary Lyapunov certification constraints, and thereafter using the automatic differentiation to update the neural policy parameters. In conjunction, our approach jointly learns a Lyapunov function that certifies the regions of state-space with stable dynamics. We also provide an initial condition sampling-based statistical guarantee for the training of NLDPC. Our offline training approach provides a computationally efficient and scalable alternative to classical model predictive control solutions, which now directly comes with Lyapunov based guarantees as shown in this paper. We substantiate the advantages of the proposed approach with simulations to stabilize the double integrator model and on an example of controlling an aircraft model.

Mukherjee, Sayak↗
DOE OSTI2025

Microgrid Building Block Architecture, and Subcomponents Modeling and Demonstration in Controller Hardware-in-the-Loop

The recently proposed Microgrid Building Blocks (MBB) are ready-to-use components to build microgrids quickly, cheaply, and modularly. This work presents and demonstrates in controller hardware-in-the-loop (CHIL) three key subcomponents of an MBB architecture: power converter topology, control scheme, and communication framework. The addressed architecture aims for systems interconnection such as microgrid to grid and microgrid to microgrid. This paper studies each subcomponent in detail and validates the efficacy of the architecture through CHIL results.

Adib, Aswad [ORNL] (ORCID:000000020997056X)↗
DOE OSTI2023

Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management

The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a two-agent soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak demand reduction. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operations to enhance control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment and reduce peak load demand from feeder's head.

deep reinforcement learning↗
DOE OSTI2022

A Multi-Branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data

A key challenge in scientific simulation is that the simulation outputs often require intensive I/O and storage space to store the results for effective post hoc analysis. This article focuses on a quality-aware adaptive temporal data selection and reconstruction problem where the goal is to adaptively select simulation data samples at certain key timesteps in situ and reconstruct the discarded samples with quality assurance during post hoc analysis. This problem is motivated by the limitation of current solutions that a significant amount of simulation data samples are either discarded or aggregated during the sampling process, leading to inaccurate modeling of the simulated phenomena. Two unique challenges exist: 1) the sampling decisions have to be made in situ and adapted to the dynamics of the complex scientific simulation data; 2) the reconstruction error must be strictly bounded to meet the application requirement. To address the above challenges, we develop DeepSample , an error-controlled convolutional neural network framework, that jointly integrates a set of coherent multi-branch deep decoders to effectively reconstruct the simulation data with rigorous quality assurance. The results on two real-world scientific simulation applications show that DeepSample significantly outperforms other state-of-the-art methods on both sampling efficiency and reconstructed simulation data quality.

Zhang, Yang↗