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At least 487 records · Page 27

All sources NASA NTRS DOE OSTI

DOE OSTI2020

In situ characterization of foreign object damage (FOD) in environmental-barrier-coated silicon carbide (SiC) ceramic

Environmental barrier coatings (EBCs) protect advanced ceramics and ceramic matrix composites (CMCs) from oxidation and corrosion in gas turbine engine environments. Foreign object damage (FOD), where debris impact the protective coatings, is a critical hazard which limits the turbine durability. Despite previous efforts to understand FOD in EBCs, a detailed understanding of the fundamental transient damage mechanisms is still lacking. In the current work, the real-time FOD behavior of a Mullite / Silicon EBC was visualized via a dynamic synchrotron X-ray source in phase contrast imaging (PCI) configuration. Prior to the in situ FOD experiments, the microstructure and composition of the coating were respectively characterized using a scanning electron microscope (SEM) and X-ray diffraction (XRD). The variation in the properties of the debris was modeled by ~ 1.5 mm diameter Partially Stabilized Zirconia (PSZ) and Silicon Nitride (Si 3 N 4 ) spheres. A modified light-gas gun setup, synchronized with the X-ray beam, was used to propel the projectiles at velocities ranging between 300 and 355 m·s -1 . Coated samples were impacted under a fully backed support configuration and at normal incidence. Coating penetration and delamination, as well as projectile deformation at the bond coat resulted for FOD by PSZ spheres. Comparatively, projectile fracture, with subsequent rebound of fragments, as well as complete coating penetration and delamination at the substrate interface occurred for FOD by Si 3 N 4 spheres. It should be noted that only cone cracking was observed for FOD by PSZ spheres while back surface cracking was present for both projectile types. Qualitative understanding of the driving forces for the observed damage mechanisms were provided.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Market Structure Evaluation to Support the Clean Energy Transition

One can argue that wholesale electricity markets in the United States are a grand experiment with scarcely two decades of operational experience in their current form. A wide body of literature points out short comings of these markets function today, ranging from problems of missing money to negative prices for real-time electricity prices. The purpose of this report is to outline a framework by which energy policies and market structures can be evaluated to ascertain the impact these institutions have on electricity generators. The report describes an evaluated framework to compare energy policy then it discusses fundamental differences in regulated versus deregulated electricity markets. Then the report leverages these sections to describe assessment needs for quantifying aggregate impacts to generators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

De-Risking Field Deployment of Power System Innovations Using Hardware-in-the-Loop Experiments

The last decade has seen needs for integrating new technologies in power system. Unlike other industries, field mis-operation of novel technologies in power system can create heavy economic losses. Thus, de-risking field deployment is critical. This webinar will provide background on technology readiness level introduced by the space industry and the use of these readiness levels in power industry to assess the maturity level of power system technologies. This webinar will discuss the different approaches used to de-risk field deployment of controller technologies and power hardware technologies. This webinar will also discuss the challenges and limitations of these de-risking approaches. Finally, the webinar will provide results from hardware-in-the-loop experiments performed at National Renewable Energy Laboratory.

controller hardware-in-the-loop↗

DOE OSTI2025

Novel technology of non-contact real-time radiation damage sensors for high power targets.

This report summarizes the contributions of an intern participating in the Community College Internship (CCI) program at Fermilab, focusing on the development of a novel, non-contact, real-time radiation damage sensor technology. The core objective is to create a reliable sensor capable of measuring radiation-induced degradation on high-power targets without physical contact. The experiment involves using a Class 3B supercontinuum laser directed toward a single material sample placed within a vacuum test chamber. The laser beam reflects off the sample's surface, with changes in reflectivity, indicative of radiation damage, measured by a spectrometer positioned at the chamber’s output port. The intern’s primary responsibilities included designing an interlock system to ensure laser operational safety, developing a camera-based monitoring system using Raspberry Pi devices, and creating structural supports using 3D modeling and printing techniques. Components for the interlock and camera systems were successfully designed and ordered, with preliminary 3D models printed and refined through iterative testing. Challenges encountered in the 3D printing process, such as fragile initial prototypes and difficult support removal, were overcome by adjusting printer settings and incorporating design enhancements like chamfered edges. Future activities, pending component delivery, involve installing and configuring the interlock and camera systems, as well as further improving the structural supports. Overall, the internship significantly enhanced the intern’s technical proficiency in hardware design, software integration, and advanced 3D printing, contributing directly to Fermilab’s operational safety standards and experimental effectiveness in high-energy physics research.

Pumarino Meza, Rafael [Unlisted; Fermilab]↗

DOE OSTI2023

Anticipating gelation and vitrification with medium amplitude parallel superposition (MAPS) rheology and artificial neural networks

Abstract Anticipating qualitative changes in the rheological response of complex fluids (e.g., a gelation or vitrification transition) is an important capability for processing operations that utilize such materials in real-world environments. One class of complex fluids that exhibits distinct rheological states are soft glassy materials such as colloidal gels and clay dispersions, which can be well characterized by the soft glassy rheology (SGR) model. We first solve the model equations for the time-dependent, weakly nonlinear response of the SGR model. With this analytical solution, we show that the weak nonlinearities measured via medium amplitude parallel superposition (MAPS) rheology can be used to anticipate the rheological aging transitions in the linear response of soft glassy materials. This is a rheological version of a technique called structural health monitoring used widely in civil and aerospace engineering. We design and train artificial neural networks (ANNs) that are capable of quickly inferring the parameters of the SGR model from the results of sequential MAPS experiments. The combination of these data-rich experiments and machine learning tools to provide a surrogate for computationally expensive viscoelastic constitutive equations allows for rapid experimental characterization of the rheological state of soft glassy materials. We apply this technique to an aging dispersion of Laponite ® clay particles approaching the gel point and demonstrate that a trained ANN can provide real-time detection of transitions in the nonlinear response well in advance of incipient changes in the linear viscoelastic response of the system.

Lennon, Kyle R. (ORCID:0000000212515461)↗

DOE OSTI2023

Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR

The integrated Resonant Magnetic Perturbation (RMP)-based Edge-Localized Mode (ELM)-crash-control process aims to enhance the plasma performance during the RMP-driven ELM crash suppression, where the RMP induces an unwanted confinement degradation. In this study, the normalized beta ($\beta_\textrm{N}$) is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance $\beta_\textrm{N}$ efficiently. The integrated process triggers the n = 1 Edge-localized RMP (ERMP) at the L–H transition timing using the real-time Machine Learning (ML) classifier. The pre-emptive RMP onset can reduce the required external heating power for achieving the same $\beta_\textrm{N}$ by over 10% compared to the conventional onset. During the RMP phase, the adaptive feedback RMP ELM controller, demonstrating its performance in previous experiments, plays a crucial role in maximizing $\beta_\textrm{N}$ during the suppression phase and sustaining the $\beta_\textrm{N}$-enhanced suppression state by optimizing the RMP strength. The integrated process achieves $\beta_\textrm{N}$ up to ~2.65 during the suppression phase, which is ~10% higher than the previous KSTAR record but ~6% lower than the target of the K-DEMO first phase ($\beta_\textrm{N}$ = 2.8), and maintains the suppression phase above the lower limit of target $\beta_\textrm{N}$ (= 2.4) for ~4 s (~60$\tau_\textrm{E}$). In addition to $\beta_\textrm{N}$ enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of $\beta_\textrm{N}$ compared to the adaptive control with the n = 1 Conventional RMP (CRMP). The post-analysis of the experiment shows the localized effect of the ERMP spectrum in radial and the close relationship between the evolution of $\beta_\textrm{N}$ and the electron temperature.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Seamless Transition of Critical Infrastructures using Droop Controlled Grid-forming Inverters

Seamless recovery of power to critical infrastructures, after grid failure, is a crucial need arising in scenarios that are increasingly becoming more frequent. Here, this article proposes a seamless transition strategy using a single and unified mode-dependent droop controlled grid-forming inverters. The control strategy achieves the following objectives: 1) regulates the output active and reactive power by the droop-controlled inverters to a desired value while operating in on-grid mode; 2) seamless transition and recovery of power injections into the load after grid failure by inverters that operates in grid-forming mode all the time; 3) requires only a single bit of information on the grid/network status for the mode transition. A framework for assessing the stability of the system and to guide the choice of parameters for controllers is developed using control-oriented modeling. A controller hardware-in-the-loop-based real-time simulation study on a test system based on the realistic electrical network of a commercial-scale medical center is conducted for initial prototyping of the control strategy. A hardware experiment is conducted with two 3 - $\phi$, 480 -V, 125 -kVA grid-forming inverters, a 3 - $\phi$, 480 -V, 270 -kVA grid simulator, a physical grid switch, and a physical load bank. The experimental data establishes the effectiveness of the always grid-forming operation and control of inverters in meeting power delivery objectives when on-grid and off-grid under various kinds of loads and scenarios while minimizing transients during transitions. Furthermore, performance comparison with existing strategies showcases the advantage of the proposed strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Delivery Ring Spill Characterization and Impulse Study

High-intensity particle physics experiments require uniform beam extraction to prevent instantaneous rate spikes from overwhelming detector systems. By analyzing accelerator parameters and extracted beam dynamics, we directly inform spill regulation systems that make real-time adjustments to minimize non-uniformity. This Department of Energy Visiting Faculty Program project transitioned from characterizing Main Injector half-integer slow extraction for SpinQuest to Delivery Ring third-integer slow extraction for Mu2e. Working alongside the Fast Adaptive Neural Control (FANC) group, we developed an automated pipeline that aligns asynchronous instrument channels, embeds quality metrics, and isolates clean spill populations. Analyzing baseline spills alongside a dedicated quadrupole impulse study allowed us to quantify noise structures while mapping time-varying beam response and transit-delay dynamics. These empirical measurements directly ground digital twin models, supporting FANC’s deployment of real-time, FPGA-based neural network controllers in the Mu2e Spill Regulation System.

Dolen, James William [Purdue U., West Lafayette] (↗

DOE OSTI2025

Real-Time Characterization of Salt Aerosols Generated from Static and Sparged Molten Salt

The formation of radionuclide-bearing aerosols in the respirable size range has the potential to significantly influence offsite dose consequences and is, therefore, an important consideration in nuclear facility safety assessments. Molten salt reactor (MSR) developers will likely need to demonstrate an understanding of the conditions under which radionuclide-bearing aerosols may be generated from their reactor under normal operating and accident conditions, as well as the characteristics and transport behavior of these aerosols, to demonstrate to the U.S. Nuclear Regulatory Commission (NRC) that the facility can be operated safely. Recent reviews of the literature identified a lack of experimental data describing the mechanisms of formation and properties (size, concentration, and composition) of salt aerosol particles that are produced from molten salts. Experiments that identify the conditions that lead to radionuclide-bearing salt aerosol releases and quantify the characteristics of salt aerosols formed by different mechanisms are high-priority needs to support MSR licensing. This report describes tests that were conducted within the Argonne Salt Aerosol Test Stand (a sealed vessel and measurement system) to generate salt aerosols from static and sparged molten salts and measure their size and concentration in real-time. The results provide insight into salt aerosol formation by the vapor condensation and bubble bursting mechanisms and inform the potential radiological consequences of aerosol formation from molten fuel salt. Videos of the salt surface were taken during salt sparge tests to observe surface bubble behavior. The data in this report can be used to develop mechanistic source term and accident progression models for MSRs. The real-time salt aerosol characterization technique used in this study will be employed in future integral effects tests that are conducted at an engineering scale to simulate realistic MSR accidents and in future separate effects tests to address additional variables that may impact salt aerosol characteristics (e.g., presence of fission products in salt and humidity in atmosphere).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Coaxial color channel focus evaluation to estimate standoff height in directed energy deposition additive manufacturing

Directed energy deposition (DED) is an additive manufacturing process that is being rapidly adopted by industry and is well suited for the fabrication of complex components in a variety of metal alloys. In laser cladding systems such as DED, powder is blown in a stream to a metal substrate coincident with a laser necessary to deposit molten metal with 3D spatial control. The focus of both the laser and the powder stream are crucial, and best deposition occurs at a predetermined standoff height between the build surface and the print head. Generally, no monitoring of this distance is implemented in commercial DED systems. Due to potential over or under building, the standoff height often changes over time but tends to self-correct. However, inexpensive and minimally intrusive methods to identify optimal standoff are required to provide real-time control to maintain the optimal distance. The present work explores the quantification of the focus of the three-color channels of a coaxial camera to determine the standoff height. An experiment was performed in which a 254 mm wall is built and the standoff height, initially 5.0 mm below the optimal position, was then intentionally increased every 25.4 mm of wall length by an amount of 1.0 mm to a final position 7.0 mm above optimal. Computer vision is demonstrated to monitor the amount of focus in each color band and estimate standoff distance. Finally, a response can be calculated in under 40 ms using simple hardware and can work in most laser-based DED systems.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Single-pulse (100 ps) extended x-ray absorption fine structure capability at the Dynamic Compression Sector

Determining real-time changes in the local atomistic order is important for a mechanistic understanding of shock wave induced structural and chemical changes. However, the single event and short duration (nanosecond times) nature of shock experiments pose challenges in obtaining Extended X-ray Absorption Fine Structure (EXAFS) measurements - typically used for monitoring local order changes. Here, we report on a new single pulse (~100 ps duration) transmission geometry EXAFS capability for use in laser shock-compression experiments at the Dynamic Compression Sector (DCS), Advanced Photon Source. We used a flat plate of highly oriented pyrolytic graphite (HOPG) as the spectrometer element to energy disperse x-rays transmitted through the sample. It provided high efficiency with approximately 15% of the x-rays incident on the HOPG reaching an x-ray area detector with high quantum efficiency. This combination resulted in good signal-to-noise ratio (~10 3 ), energy resolution of ~10 eV at 10 keV, EXAFS spectra covering 100s of eV, and good pulse to pulse reproducibility of our single pulse measurements. Ambient EXAFS spectra for Cu and Au are compared to reference spectra, validating our measurement system. Comparison of single pulse EXAFS results for ambient and laser shocked Ge(100) shows large changes to the local structure of the short lived state of shocked Ge. The current DCS EXAFS capability can be used to perform single pulse measurements in laser shocked materials from approximately 9-13 keV. These EXAFS developments will be available to all users of the DCS.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Multirobot Collaborative Pursuit Target Robot by Improved MADDPG

Policy formulation is one of the main problems in multirobot systems, especially in multirobot pursuit-evasion scenarios, where both sparse rewards and random environment changes bring great difficulties to find better strategy. Existing multirobot decision-making methods mostly use environmental rewards to promote robots to complete the target task that cannot achieve good results. This paper proposes a multirobot pursuit method based on improved multiagent deep deterministic policy gradient (MADDPG), which solves the problem of sparse rewards in multirobot pursuit-evasion scenarios by combining the intrinsic reward and the external environment. The state similarity module based on the threshold constraint is as a part of the intrinsic reward signal output by the intrinsic curiosity module, which is used to balance overexploration and insufficient exploration, so that the agent can use the intrinsic reward more effectively to learn better strategies. The simulation experiment results show that the proposed method can improve the reward value of robots and the success rate of the pursuit task significantly. The intuitive change is obviously reflected in the real-time distance between the pursuer and the escapee, the pursuer using the improved algorithm for training can get closer to the escapee more quickly, and the average following distance also decreases.

Zhou, Xiao↗

DOE OSTI2020

Surrogate Suspension-Resuspension Study

The objective of this study was to conduct a series of tests looking at the deposition and resuspension of aerosol particles deposited onto multiple representative substrate surfaces for a range of particle sizes under varying environmental conditions. The benefit of this study is to provide additional insight into the understanding of early time resuspension from different mechanisms and compare to existing literature. The resuspension methods utilized in this study were full-scale and the substrates were representative of real- world ground level conditions. Multiple experiments were conducted to assess the impact on resuspension from the varying substrates and mechanisms. The results of this study show variations in the size distribution of aerosol as a function of height from the source resuspension factors. Additionally, the aerosolized mass concentration and resuspension factor were evaluated. The maximum resuspension factor was found to be on the order of 10 -4 m -1 which is higher than most resuspension factors found in literature but represents idealized conditions due to the well constrained experimental setup.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Explainability and human intervention in autonomous scanning probe microscopy

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Implicit neural representations for experimental steering of advanced experiments

Scattering measurements using electrons, neutrons, or photons are essential for obtaining microscopic insights into materials. However, limited facility availability and high-dimensional scattering data necessitate more efficient experimental steering techniques. Here, we report a machine learning method that guides scattering data collection and facilitates real-time estimation of model parameters, given a reliable forward model to simulate experimental signals. We employ implicit neural representations as efficient surrogates that link model parameters with simulated spectroscopies. This enables a Bayesian optimal experimental design framework to estimate the probability distributions of parameters from high-dimensional scattering data. We demonstrate the proposed method using inelastic neutron scattering with simulated and real experimental data, highlighting the method’s ability to provide real-time parameter estimation with quantified uncertainties and to deliver informed experimental guidance that reduces experimental time while maximizing scientific output. This approach paves the way for accelerated discoveries in condensed matter through scattering measurements.

36 MATERIALS SCIENCE↗

DOE OSTI2025

LCLS Big Data Handling – How I Learned to Stop Worrying and Love the Data Deluge

Advanced data and computing systems are vital to Linac Coherent Light Source (LCLS) operations, data interpretation and overall scientific productivity. The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation. The LCLS-II Data System meets big data challenges by implementing configurable data reduction that can adapt to multiple science areas, real-time analysis frameworks to provide visualization and fast feedback, and the ability to transfer data to local and remote computational facilities for near real time analysis at the appropriate scale. Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote High-Performance Computing (HPC) resources - can be used to steer experiments and inform user decisions during beam time. Artificial Intelligence and Machine Learning (AI/ML) techniques present new opportunities to rapidly analyse large datasets and direct experiments, but create new challenges in scaling, adaptability, complexity, and trustworthiness. We describe how the LCLS-II Data System architecture addresses its data-driven challenges in the areas of data acquisition, data processing, data management, and workflow orchestration to decrease the overall time-to-science and provide a vision for future developments.

artificial intelligence↗

DOE OSTI2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML) and silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

Gonski, Julia [SLAC]↗

DOE OSTI2023

Agricultural Ice Nuclei at Southern Great Plains Supplemental Sampling (AGINSGP-SUPP) Field Campaign Report

The Agricultural Ice Nuclei at Southern Great Plains Supplemental Sampling (AGINSGP-SUPP) field campaign took place from April 8 to April 28, 2022. It included sample collection and analysis of filter samples for ice-nucleating particle (INP) concentrations. By enabling quantification of INPs at warmer temperatures (i.e., T > 258 K), these samples complemented the real-time observations of INPs that were performed as part of the AGINSGP campaign during the same period. In addition to collecting ground-based samples, the campaign collected vertical samples of INPs aboard the Atmospheric Radiation Measurement (ARM) tethered balloon system (TBS) as shown in Figure 1. These vertical samples complemented the vertical profiling of aerosol properties that was supported under a related Facilities Integrating Collaborations for User Science (FICUS) proposal. The overall aim of these three campaigns was to improve understanding of the sources and variability in observed INP concentrations at ARM’s SGP observatory. For example, warm-temperature INPs in this agricultural region are thought to originate in part from regional emissions of fertile, agricultural soils containing high organic content. Laboratory experiments have shown that soil particles with high organic content are more efficient INPs than inorganic soils (e.g., Tobo et al. 2014). Quantification of warm-temperature INPs under ambient conditions currently can only be achieved via offline methods such as the ARM ice-nucleation spectrometer (INS). Therefore, offline sampling for the INS was required to complement the real-time INP measurements conducted during the AGINSGP campaign.

, tethered balloon system↗