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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Developing a Digital Twin for SRF Cavity Assembly at Fermilab

When assembling Superconducting Radio Frequency (SRF) Cavities, maintaining an environment devoid of particulates like dust and other small particles is essential. If a single spec of dust enters the cavity a significant degradation of performance can occur. To avoid a cavity failure Fermilab assembles the SRF cavities within a ISO-4 (Class 10) environment. This environment though is still susceptible to foreign contaminants when technicians enter and new components are added to the cleanroom. To reduce the risk even more Fermilab has introduced a cobot manipulator into the cleanroom environment to speed up the assembly time which will reduce the time that the technicians operate in the cleanroom. But, this still leaves the potential of contaminants to enter the cleanroom if new components need to be tested within the cleanroom. This project aims to lay the groundwork to develop a Digital Twin environment of the cleanroom to aid in manufacturing processes and testing. In its simplest form, a digital twin is a bidirectional link between a physical system and its digital counterpart or twin. NVIDIA Isaac Sim is used as the digital twin foundation for the digital representation of the cleanroom, specifically for the UR16e assembly area. A simulated UR16e was used to validate the performance of Isaac Sim as a testing environment by comparing the tool center points (TCP) positional data between the simulated and digital representation of the UR16e. Due to a new vision based robotic assembly process being introduced to the cleanroom a digital representation of the physical camera was tested and validated to ensure that it will produce close to the same outcome as the physical environment. The TCP comparison results showed a peak translational error of approximately 0.1mm and rotational errors of up to 8 between the simulated and digital UR16es. While the camera validation performed with high repeatability across multiple runs, it still requires minor tuning before it can accurately replicate a physical camera.

Imburgia, Joseph [Northern Illinois U.]↗
DOE OSTI2021

TEMPO allegro: liquid catholyte redoxmers for nonaqueous redox flow batteries

Redoxmers are organic active molecules storing energy in redox flow batteries (RFBs). Liquid redoxmers represent an extreme scenario where maximum concentration may be achieved by minimizing supporting solvents, thus maximizing the energy density of RFBs. Herein, a series of (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO)-based high potential (catholyte) liquid redoxmers, TEMPO-EG1, TEMPO-EG2, and TEMPO-EG3, were developed by incorporating polyethylene glycol (PEG) chains. Such modifications not only afford dramatic physical changes from solid to liquid and full miscibility in acetonitrile, but also impact the redox behavior. DFT calculations indicate that the incorporated PEG chains impact the charge distribution, which may account for the electrochemical changes. Importantly, compared to our previous liquid catholytes, the new redoxmers exhibit lower viscosity, which is desired for enhancing high concentration cycling performance. Furthermore, by using a hybrid flow cell, TEMPO-EG1 demonstrated more than 70% capacity retention over 100 cycles at 0.1 M and 66% capacity retention at 0.5 M, affording excellent cyclability at various concentrations. The study exemplifies how molecular engineering tuned the rheological properties of redoxmers, such as viscosity, to improve the high concentration cycling performance of RFBs, which may represent a promising avenue for a high energy density and low-cost flow battery system.

25 ENERGY STORAGE↗
DOE OSTI2020

Local Phasor-Based Control of DER Inverters for Voltage Regulation on Distribution Feeders

We introduce a new control paradigm termed Phasor-Based Control (PBC) to coordinate Distributed Energy Resources (DER) for improved voltage regulation and other objectives. PBC is a multi-layer control framework where the state of the distribution grid is represented by voltage phasors, and Phasor Measurement Units (PMUs) measure the state at critical points in the distribution grid. In the PBC paradigm, a supervisory controller (S-PBC) sets phasor targets which local controllers track. In this paper, we propose a Proportional Integral (PI) implementation for the local control layer (LPBC), to regulate real and reactive power output of distributed inverters to track a voltage phasor target computed by the supervisory layer. We tune the PI controller gains offline with a genetic algorithm that yields better performance than the Ziegler-Nichols method. We benchmark the proposed L-PBC controller against droop volt-var control (DVVC) and observe improved voltage regulation in simulation on the IEEE 13 node unbalanced test feeder (IEEE 13NF). Furthermore, by means of numerical simulation we observe the response of the L-PBC controllers to small and large grid disturbances. Simulations on larger feeders, IEEE 123-node and 647-node, demonstrate challenges with tuning controllers in setups with different amounts of power-voltage coupling.

42 ENGINEERING↗
DOE OSTI2022

Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning

Cover cropping between cash crop growing seasons is a multifunctional conservation practice. Timely and accurate monitoring of cover crop traits, notably aboveground biomass and nutrient content, is beneficial to agricultural stakeholders to improve management and understand outcomes. Currently, there is a scarcity of spatially and temporally resolved information for assessing cover crop growth. Remote sensing has a high potential to fill this need, but conventional empirical regression operated with coarse-resolution multispectral data has large uncertainties. Therefore, this study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring. Specifically, we deployed an airborne hyperspectral system covering visible to shortwave-infrared wavelengths (400–2400 nm) to acquire high spatial (0.5 m) and spectral (3–5 nm) resolution reflectance over 23 cover crop fields across Central Illinois in March and April of 2021. Airborne hyperspectral surface reflectance with high spectral and spatial resolution can be well matched with field data to quantify cover crop traits. Furthermore, the PGML models were pre-trained by synthetic data from soil-vegetation radiative transfer modeling (one million records), and then fine-tuned with field data of cover crop biomass and nutrient content. Results show that airborne hyperspectral data with PGML can achieve high accuracy to predict cover crop aboveground biomass (R 2 = 0.72, relative RMSE = 15.16%) and nitrogen content (R 2 = 0.69, relative RMSE = 16.59%) through leave-one-field-out cross-validation. Unlike the pure data-driven approach (e.g., partial least-squares regression), PGML incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. Meanwhile, with field data for model fine-tuning, PGML predicted biomass more accurately than the inversion of radiative transfer models. Here we also found that the red edge has a high contribution in quantifying aboveground biomass and nitrogen content, followed by green and shortwave spectra. This study demonstrated the first attempt of utilizing hyperspectral remote sensing to accurately quantify cover crop traits. We highlight the strength of PGML in exploiting sensing data to quantify ecosystem variables to advance agroecosystem monitoring for sustainable agricultural management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

OC7 phase I: Toward practical sea-state-dependent modeling of hydrodynamic viscous drag and damping

Here, this article presents a collaborative research campaign under the OC7 project on refining the engineering modeling approach for hydrodynamic viscous drag and damping on floating wind platforms, focusing on the adjustment of hydrodynamic drag and damping coefficients for different sea states. The participant simulation results show significant improvements over the previous OC6 project in predicting the low-frequency resonance motion under nonoperational conditions. The improvements are mainly due to enhanced modeling, including the adoption of wave stretching, and directly tuning the coefficients to measured platform motion in waves instead of free decay. For accurate predictions of mean- and slow-drift motion, the better performing models use a decreasing column splash zone drag coefficient and increasing surge damping/drag with increasing wave height. The model tuning for heave and pitch resonance shows less consistency. Generally, both quadratic drag and additional heave or pitch damping are needed for accurate predictions. Alternatively, a quadratic drag formulation with velocity filtering for the rectangular pontoons leads to improved predictions without additional damping. This model is also potentially more predictive, requiring minimal adjustment to its parameters for different conditions.

17 WIND ENERGY↗
DOE OSTI2020

Enhancing Graphene Plasmonic Device Performance via its Dielectric Environment

Graphene plasmons provide a compelling avenue toward chip-scale dynamic tuning of infrared light. Dynamic tunability emerges through controlled alterations in the optical properties of the system defining graphene’s plasmonic dispersion. Typically, electrostatic induced alterations of the carrier concentration in graphene working in conjunction with mobility have been considered the primary factors dictating plasmonic tunability. We find here that the surrounding dielectric environment also plays a primary role, dictating not just the energy of the graphene plasmon but so too the magnitude of its tuning and spectral width. To arrive at this conclusion, poles in the imaginary component of the reflection coefficient are used to efficiently survey the effect of the surrounding dielectric on the tuning of the graphene plasmon. By investigating several common polar materials, optical phonons (i.e., the Reststrahlen band) of the dielectric substrate are shown to appreciably affect not only the plasmon’s spectral location but its tunability, and its resonance shape as well. In particular, tunability is maximized when the resonances are spectrally distant from the Reststrahlen band, whereas sharp resonances (i.e., high-Q) are achievable at the band’s edge. Overall, these observations both underscore the necessity of viewing the dielectric environment in aggregate when considering the plasmonic response derived from two-dimensional materials and provide heuristics to design dynamically tunable graphene-based infrared devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

LibraryX: A Framework for Cross-Library-Call Optimization

Scientific applications utilize performance libraries as a software engineering concept: these libraries encapsulate important and well-understood (mathematical) operations, allow for reuse, and are implemented and tuned by experts. Domain scientists then implement complex algorithms based on these domainspecific libraries. While individual library calls are optimized, larger performance gains across sequences of calls—sometimes spanning multiple libraries—are often unrealized, forcing a trade-off between performance and implementation complexity.To overcome this issue, we propose LibraryX, an approach and a system that allows for cross-library-call optimization even when library calls stem from multiple performance libraries. LibraryX annotates library calls with semantic information and optimizes entire directed acyclic graphs (DAGs) of calls dynamically using the SPIRAL code generation system. We demonstrate its effectiveness across a range of memory bound workloads, achieving significant speedups on Nvidia, AMD, and Intel accelerators compared to code using native libraries without cross-call optimization.

Rao, Sanil [Carnegie Mellon University,Department ↗
NASA NTRSDate not supplied

Strategies for Automation of Model Tuning in Multifidelity Trajectory Uncertainty Propagation

Multi-model Monte Carlo methods are efficient strategies to perform forward uncertainty quantification studies in entry, descent, and landing (EDL) applications. These multi-model methods are based on the classical Monte Carlo estimator, but fuse predictions from several low-fidelity models to obtain estimators with greater precision given a prescribed computational budget. The effectiveness of these approaches relies on the magnitudes of correlations between the low-fidelity models and the high-fidelity model, as well as the relative computational costs of all models. Identifying and exploiting the best trade-off between correlation and cost, which ultimately depends on the selection of hyperparameters in the low-fidelity models, is a task often performed by hand or simply inspired by the deterministic understanding available for a specific application. This work extends a preliminary effort,

Marten Thompson↗
NASA NTRS1993

Neural self-tuning adaptive control of non-minimum phase system

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

Ho, Long T.↗
DOE OSTI2025

Visual Instance-aware Prompt Tuning

Visual Prompt Tuning (VPT) has emerged as a parameter-efficient fine-tuning paradigm for vision transformers, with conventional approaches utilizing dataset-level prompts that remain the same across all input instances. We observe that this strategy results in sub-optimal performance due to high variance in downstream datasets. To address this challenge, we propose Visual Instance-aware Prompt Tuning (ViaPT), which generates instance-aware prompts based on each individual input and fuses them with dataset-level prompts, leveraging Principal Component Analysis (PCA) to retain important prompting information. Moreover, we reveal that VPT-Deep and VPT-Shallow represent two corner cases based on a conceptual understanding, in which they fail to effectively capture instance-specific information, while random dimension reduction on prompts only yields performance between the two extremes. Instead, ViaPT overcomes these limitations by balancing dataset-level and instance-level knowledge, while reducing the amount of learnable parameters compared to VPT-Deep. Extensive experiments across 34 diverse datasets demonstrate that our method consistently outperforms state-of-the-art baselines, establishing a new paradigm for analyzing and optimizing visual prompts for vision transformers.

Xiao, Xi [ORNL] (ORCID:0009000009316982)↗
DOE OSTI2025

A sub-5 ps jitter time-to-digital converter ASIC with back-gate delay tuning in 22 nm CMOS

We present the design of a Time-to-Digital Converter (TDC) ASIC together with performance characterization results at cryogenic temperature (6-8 K), and at room temperature using emulated Low-Gain Avalanche Detector (LGAD) signals. The TDC design uses a two-step architecture with a ring-oscillator based counter and a Vernier delay line fine TDC. The TDC is implemented in an FD-SOI process, and back-gate tuning is used not only to correct threshold variation due to cryogenic operation, but also as a novel way to tune TDC delay elements with very little overhead. Using this technique, we demonstrate a design with very low power (0.5 mW) and area (0.003 mm 2 ). We present test results using the Fermilab Constant Fraction Discriminator (FCFD) ASIC to produce discriminated signals from an internal charge injection mechanism that mimics the waveform and signal amplitude of minimum ionizing particles impinging on LGAD sensors. We characterize the time precision of the full system and verify that the TDC ASIC contributes a negligible amount to the total system time precision, fully consistent with the expected jitter contribution of less than 5 ps. In conclusion, the results presented here demonstrate the utility of our TDC for applications in physics and quantum communications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2011

Experiments at SRT Using the NOAA CrIS/ATMS Proxy Data Set

The objectives of the talk are: (1) Assess the performance of NGAS Version-1.5.03.00 CrIS/ATMS retrieval algorithm as delivered by LaRC, modified to include the MW and IR tuning coefficients and new CrIS noise model (a) Percent acceptance (b) RMS and mean differences of T(p) vs. ECMWF truth as a function of % yield (2) Compare performance of NGAS retrieval algorithm with an AIRS Science Team Version-6 like retrieval algorithm modified at Sounder Research Team (SRT) for CrIS/ATMS

Susskind, Joel↗
DOE OSTI2023

High-Performance Passivating Contacts for Si PV Based on Engineered, Doped Nanopinholes through Dielectric Layers

We present a novel, industrially relevant method to fabricate high-performance poly-Si passivating contacts for Si PV. Passivating contacts based on SiOx/poly-Si layer stacks have been implemented in record-efficiency homojunction Si solar cells. In this technology, the carrier transport through a surface-passivating SiOx is enabled either by quantum-tunneling or nanopinholes. The doped poly-Si layer provides charge-carrier selectivity. Previously, the ISFH research group demonstrated a record 26.1% efficient Si homojunction cell, where transport pinholes in ~2 nm SiOx were produced by thermal breakdown at > 1000 degrees C, which is hard to control and is surface morphology-dependent. At room temperature, our new process uses electroless plating of Ag nanoparticles, followed by metal-assisted chemical etching. Nanogalvanic corrosion yields < 20 nm-wide nanopinholes in an insulating > 2 nm SiOx layer. The nanopinholes are filled by a heavily doped a-Si:H overlayer. We drive in the dopants into the Si wafer with subsequent high-temperature annealing. Preferential incorporation of dopants at pinhole locations results in nanoscale p+/n or n+/n junctions near the wafer interface. Each heavily-doped nanoscale pinhole junction collects and transports photogenerated carriers to the cell metal contacts. The density of the engineered nanopinholes can be tuned over a wide range which is critical for optimization of device performance. The same nanogalvanic corrosion principles can be applied to produce doped transport pinholes in well-passivating thick dielectric SiOx/SiNy stacks. Importantly, our process does not rely on thermal breakdown of the dielectric layers and can be applied to textured wafers resulting in Si solar cells with > 20% conversion efficiency.

dielectric layer↗
DOE OSTI2025

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2009

Adaptive Control of Small Outboard-Powered Boats for Survey Applications

Four autopilot controllers have been developed in this work that can both hold a desired heading and follow a straight line. These PID, adaptive PID, neuro-adaptive, and adaptive augmenting control algorithms have all been implemented into a numerical simulation of a 33-foot center console vessel with wind, waves, and current disturbances acting in the perpendicular (across-track) direction of the boat s desired trajectory. Each controller is tested for its ability to follow a desired heading in the presence of these disturbances and then to follow a straight line at two different throttle settings for the same disturbances. These controllers were tuned for an input thrust of 2000 N and all four controllers showed good performance with none of the controllers significantly outperforming the others when holding a constant heading and following a straight line at this engine thrust. Each controller was then tested for a reduced engine thrust of 1200 N per engine where each of the three adaptive controllers reduced heading error and across-track error by approximately 50% after a 300 second tuning period when compared to the fixed gain PID, showing that significant robustness to changes in throttle setting was gained by using an adaptive algorithm.

VanZwieten, T.S.↗
NASA NTRS2018

A Learn-To-Fly Approach for Adaptively Tuning Flight Control Systems

A method is presented for adaptively tuning feedback control gains in a ight control sys- tem to achieve desired closed-loop performance. The method combines efficient parameter estimation for identifying closed-loop dynamics models, with online nonlinear optimization for sequentially perturbing and updating control gains to improve performance. Prior in- formation on stability and control derivatives is not needed, nor is any knowledge about the control system architecture. Following convergence, the optimized control gains (with uncertainties), the open-loop dynamics model, and the closed-loop dynamics model are available. The method is demonstrated for tuning a longitudinal stability augmentation system using a realistic nonlinear ight dynamics simulation of the NASA FASER airplane. Convergence was attained using five piloted maneuvers that spanned approximately one minute of ight test time. Although demonstrated for a relatively simple case, the method is general and can be applied to other aircraft, axes, performance metrics, and control systems.

Grauer, Jared A.↗
NASA NTRSDate not supplied

Thin-wall Internal Channel Geometry and Surface Enhancements for Heat Exchangers using Laser Powder Directed Energy Deposition

Additive Manufacturing (AM) has offered many new design and manufacturing opportunities for components across various industries. As AM evolves there is a need to better understand outputs of the process including geometric limitations, surface texture, and post-processing surface enhancements for specific application requirements. One possible application area of AM are components using thin-wall (~1 mm) microchannel heat exchangers for subsystems across aerospace and industrial applications. Laser Powder Bed Fusion (L-PBF) is a common process for complex internal channels but the build diameter is limited to approximately 600 mm. Laser Powder Directed Energy Deposition (LP-DED) is being evaluated to produce thin-wall microchannel heat exchangers at scales beyond the L-PBF process. Successful deployment of the LP-DED technology requires characterization of geometric features from the process and potential improvements to the surface using post-processing. Surface texture, inclusive of roughness and waviness, is one of the critical attributes of AM that effects the friction factor and pressure drop within a heat exchanger and lacks data for the LP-DED process. This presentation will provide an overview of the characterization work completed of the LP-DED process for thin-walls and small channel geometry representative of various high performance alloys including NASA HR-1 and GRCop-42. An overview of the experiments conducted with varying LP-DED parameters, evaluation of various internal channel geometry, geometric build features, and resulting surface texture will be provided along with a summary of conclusions from these experiments. This study presents characterization of 2.5 mm microchannels using LP-DED, mechanisms that cause the surface texture which include powder adherence and material droop, and angled walls have a significant impact on the thickness and surface texture. Results will also be presented on various surface enhancement processes that allow for tuning of the wetting surface for friction factor, heat transfer, or fatigue life performance requirements.

Additive Manufacturing↗