Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “performance bound”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Crack Identification and Characterization in Deformed Nb3Sn Rutherford Cable Stacks Using Machine Learning

An investigation of instance segmentation of cracks in Nb3Sn 4-stack 40-strand Rutherford cables using machine learning is presented. Three samples were uniaxially and biaxially loaded before metallographic inspections were performed. The Mask R-CNN model was used in the Detectron2 framework with pre-trained weights but fine-tuned to detect and segment cracks. The model detected cracks with bounding box and mask average precisions (AP) of 42.8 and 27.9, respectively, and was used for instance segmentation of all cracks in the three samples. More cracks were found in the sample pre-loaded along the z-axis (i.e., along the cable length). Pre-loading along the x-axis (i.e., on the cables edges) reduced the number of cracks and changed the crack orientation distribution, away from being highly aligned with the y-axis (i.e., normal to the cables broad faces), i.e., the direction with the highest applied load. Fine-tuning of the Segment Anything Model (SAM) was also studied but performed poorly without human-provided prompts. However, the zero-shot capability of SAM showed high promises to accelerate the image annotation process for applications beyond this study.

Croteau, Jean-Francois↗
DOE OSTI2023

Entanglement between valence and sea quarks in hadrons of 1+1 dimensional QCD

The conceptual interpretation of valence- and sea-quark separation, which is a key aspect of the parton model and of an intuitive picture of hadron structure, becomes obscured by quantum effects in QCD. This suggests that there may be measures of entanglement between quark degrees of freedom that are present in QCD, but absent in the intuitive picture with a clear valence-sea (VS) separation. In this paper, we define the first rigorous measure of VS entanglement in QCD in an attempt to bring conceptual clarity to this issue, and, potentially, to find a measure of the applicability of the parton model to QCD bound states. This VS entanglement vanishes in the large-N c limit, and it remains low when finite-N c states resemble their large-N c counterparts. We perform a numerical study of VS entanglement in 1 + 1 dimensional discrete light-cone quantized QCD, and in the process develop a method for building the color-singlet basis of 1 + 1d QCD that is manifestly complete and orthogonal by construction. We calculate this VS entanglement entropy for the first time and find that it is relatively low for the first few excited states of both mesons and baryons compared to all other states in the spectrum, with the VS entropy of ground state hadrons providing a minimum. We also see that for ground state mesons the entropy is well described in the 1/N c approximation. These results suggest that low energy hadrons may be the only QCD bound states for which the large-N c expansion, and perhaps the parton model, provide an accurate description. This work also provides the first evidence that the VS entanglement entropy of QCD in 3 + 1d, which would likely serve as an order parameter for the transition between quark and hadron degrees of freedom, may be perturbatively accessible through a large-N c expansion.

1+1 dimensional QCD↗
DOE OSTI2025

Lower bounds on entanglement entropy without twin copy

We discuss the possibility of estimating experimentally the von Neumann entanglement entropy S A v N of a symmetric bipartite quantum system A B by using the basic measurement counts (bitstrings) for a single copy of a prepared state. Using exact diagonalization and analog simulations performed with the publicly available QuEra facilities for chains and ladders of Rydberg atoms, we calculate the Shannon entropy S A B X associated with the bitstrings of adiabatically prepared ground states and the reduced entropies S A X and S B X obtained from the marginal probabilities in A and B . We then calculate the classical mutual information I A B X = S A X + S B X − S A B X , which is a lower bound on S A v N . We show that for a broad range of lattice spacing and detuning, I A B X is typically 20% below S A v N in regions where S A v N is large and a less close bound in regions where S A v N is low. We argue that this use of the easily available bitstrings provides a robust and efficient way to explore empirically the phase diagram of qubit-based quantum simulators and identify critical regions. Published by the American Physical Society 2025

Meurice, Yannick (ORCID:0000000209959694)↗
DOE OSTI2026

BISON analyses of TRISO fuel performance, its dependence on time-at-temperature, and possible implications for fuel design and qualification

The Advanced Gas Reactor Fuel Development and Qualification (AGR) program has established a substantial technical foundation to support private entry into the U.S. high-temperature gas-cooled reactor market. However, emerging tristructural isotropic (TRISO)-fueled reactor applications include small modular reactors and microreactors with longer fuel residence times, which may expose fuels to higher time-at-temperature (TAT) values than were explored by the AGR program. Increased TAT could affect diffusive and thermomechanical behaviors such as Pd penetration, fission gas release, creep, and fission product transport. In this work, we applied multiscale best-estimate BISON fuel performance modeling to assess these effects within a representative design space based on the AGR-5/6/7 experiment and analyzed trends in predicted particle and compact fuel performance metrics with possible implications for near-term fuel design and qualification. BISON unambiguously predicted that TRISO fuel performance is sensitive to TAT. Increasing TAT was not predicted to increase the magnitude of failure-inducing tangential stresses in particle coating layers. Predictions obtained using a mechanistic model for Pd penetration indicated that penetration depth does not depend strongly on TAT. While these observations suggest that AGR testing provides a conservative upper bound for the steady-state operation of TRISO particles at lower powers and higher residence times, BISON also predicted that the release of poorly retained Ag would increase with TAT. Because these analyses applied models to extrapolate beyond the available experimental data, the authors recommend performing targeted experiments to confirm these predictions. Nevertheless, these predictions may provide reactor developers with enough confidence to make near-term design decisions associated with the potential fuel performance trade-offs of increasing TAT.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Unraveling In-Situ Formation of Surface Nickel Nitride Structures in Plasma-Assisted Catalytic Ammonia Synthesis

We report the in situ formation of Ni nitride for plasma-assisted ammonia synthesis. Both the surface nitrogen concentration and the ammonia formation rate exhibit dependence on the N 2 :H 2 feed ratio. The maximum surface nitrogen concentration occurs at a N 2 :H 2 ratio of 4:1, and the maximum catalytic activity occurs at 2:1. In contrast, the formation of gas phase radicals is less sensitive to feed composition, indicating that Ni nitride is more kinetically relevant to ammonia production than gas-phase radicals. The plasma-induced formation of Ni nitride is therefore proposed to be a critical contributor to the synergistic effects in plasma-assisted catalytic ammonia synthesis. Additionally, Ni nitride alters the surface reaction mechanism of plasma-assisted ammonia synthesis, with the rate-determining-step (RDS) shifting to surface-bound NH 3 formation rather than N 2 activation at temperatures below 373 K. These findings provide mechanistic insight that opens opportunities for optimizing the performance of plasma-assisted catalytic ammonia synthesis

Ammonia↗
DOE OSTI2024

Double-pion electroproduction off protons in deuterium: Quasifree cross sections and final-state interactions

Here, the single-differential and fully integrated cross sections for quasi-free $π^+π^-$ electroproduction off protons bound in deuterium have been extracted for the first time. The experimental data were collected at Jefferson Laboratory with the CLAS detector. The measurements were performed in the kinematic region of the invariant mass W from 1.3 GeV to 1.825 GeV and photon virtuality Q 2 from 0.4 GeV2 to 1.0 GeV 2 . Sufficient experimental statistics allow for narrow binning in all kinematic variables, while maintaining a small statistical uncertainty. The extracted cross sections were compared with the corresponding cross sections off free protons, which allowed us to obtain an estimate of the contribution from events in which interactions between the final-state hadrons and the spectator neutron took place.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Barrier Function-based Reinforcement Learning for Emergency Control of Power Systems

Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe conditions. However, this scheme usually trips a massive amount of load which can be unnecessary and harmful to customers. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach that can significantly reduce the amount of load shedding. However, like most existing machine learning (ML)-based control techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel safe RL method for load shedding of power systems, that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Unlike standard RL method, the safe RL method integrates a Barrier function into the reward function. Consequently, the optimal control policy can render the power system to avoid the safety bounds. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

Vu, Thanh Long↗
DOE OSTI2021

Nonlinear Multiple Models Adaptive Secondary Voltage Control of Microgrids

This article proposes a model-free secondary voltage control (SVC) for microgrids (MG) using nonlinear multiple models adaptive control. Firstly, a linear robust adaptive controller is designed to guarantee the voltage stability in the bounded-input- bounded-output (BIBO) manner so as to meet the operation requirements of MGs. Secondly, a nonlinear adaptive controller is developed to improve the voltage tracking performance with the help of artificial neural networks (ANNs). A switching mechanism for coordinating such two controllers is designed to guarantee the closed-loop stability while achieving accurate voltage tracking. Here, by an online identification based on the input and output data of MGs, the proposed method does not resort to any apriori information of system model and primary control, thus exhibiting good robustness, ease of deployment and disturbance rejection.

17 WIND ENERGY↗
DOE OSTI2020

Panel-Segmentation [SWR-21-18]

Panel-Segmentation contains the scripts for automated metadata extraction of solar PV installations, using satellite imagery coupled with computer vision techniques. In this package, the user can perform the following actions: *Automatically generate a satellite image using a set of lat-long coordinates, and a Google Maps API key. Users would need to set up a Google Cloud account and get a Maps Static API key. Please refer to Setting Up Google Maps Static API Key section for this process. *Perform image segmentation on the satellite image, to locate the solar array(s) in the image on a pixel-by-pixel basis, using an image segmentation model (panel_detection_model.pth). Get classification of the installation (rooftop, ground mounted fixed-tilt or tracking, carport, etc). *Perform azimuth estimation on each solar array cluster in the masked image. *Detect solar panels and get its latitude, longitude, and address within a geographic bounding box through the SOL-Searcher Pipeline. *Detect and calculate hurricane damage on solar installations given pre-hurricane and post-hurricane satellite imagery through the Hurricane Detection Pipeline. *Detect and calculate hail damage on solar installations given satellite imagery through the Hail Detection pipeline. *Convert NOAA MESH (Maximum Estimated Size of Hail) grib2 files into kml or geojson files. *Estimate tilt and azimuth of a solar array by processing USGS LiDAR data for the array’s location.

Edun, Ayobami↗
DOE OSTI2021

Transparent Object Tracking Benchmark

Visual tracking has achieved considerable progress in recent years. However, current research in the field mainly focuses on tracking of opaque objects, while little attention is paid to transparent object tracking. In this paper, we make the first attempt in exploring this problem by proposing a Transparent Object Tracking Benchmark (TOTB). Specifically, TOTB consists of 225 videos (86K frames) from 15 diverse transparent object categories. Each sequence is manually labeled with axis-aligned bounding boxes. To the best of our knowledge, TOTB is the first benchmark dedicated to transparent object tracking. In order to understand how existing trackers perform and to provide comparison for future research on TOTB, we extensively evaluate 25 state-of-the-art tracking algorithms. The evaluation results exhibit that more efforts are needed to improve transparent object tracking. Besides, we observe some nontrivial findings from the evaluation that are discrepant with some common beliefs in opaque object tracking. For example, we find that deeper features are not always good for improvements. Moreover, to encourage future research, we introduce a novel tracker, named TransATOM, which leverages transparency features for tracking and surpasses all 25 evaluated approaches by a large margin. By releasing TOTB, we expect to facilitate future research and application of transparent object tracking in both the academia and industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Nonlocal-Gradient Descent Method for Inverse Design in Nanophotonics

Local-gradient-based optimization approaches lack nonlocal exploration abilityrequired for escaping from local minima when searching non-convex landscapes.A directional Gaussian smoothing (DGS) approach was recently proposed in [29]and used to define a truly nonlocal gradient, referred to as the DGS gradient, inorder to enable nonlocal exploration in high-dimensional black-box optimization.Promising results show that replacing the traditional local gradient with the nonlocalDGS gradient can significantly improve the performance of gradient-based methodsin optimizing highly multi-modal loss functions. However, the current DGS methodis designed for unbounded and uncontrained optimization problems, making itinapplicable to real-world engineering optimization problems where the tuningparameters are often bounded and the loss function is usually constrained byphysical processes. In this work, we propose to extend to the DGS approachto the constrained inverse design framework in order to find better optima ofmulti-modal loss functions. A series of adaptive strategies for smoothing radiusand learning rate updating are developed to improve the computational efficiencyand robustness. Our methodology is demonstrated by an example of designing ananoscale wavelength demultiplexer, and shows superior performance compared tothe state-of-the-art approaches. By incorporating volume constraints, the optimizeddesign achieves an equivalently high performance but significantly reduces theamount of material usage.

Bi, Sirui↗
DOE OSTI2022

Bayesian Framework for Multi-Timescale State Estimation in Low-Observable Distribution Systems

To support the smart grid paradigm, there has been a significant increase in sensor deployments and metering infrastructure in distribution systems. However, the measurements provided by these sensors and metering devices are typically sampled at different rates and could suffer from losses during the aggregation process. It is crucial to effectively reconcile the time-series measurements for a reliable state estimation. While weighted least squares has been the traditional approach for state estimation, sparsity-based approaches like matrix completion have become popular due to their superior performance in low-observability conditions. This paper proposes a Bayesian framework for both multi-timescale data aggregation and matrix completion based state estimation. Specifically, the multiscale time-series data aggregated from heterogenous sources are reconciled using a multitask Gaussian process that exploits the spatio-temporal correlations. Here, the resulting consistent timeseries alongwith the confidence bound on the imputations are fed into a Bayesian matrix completion method augmented with linearized power-flow constraints to accurately estimate the states in low-observability conditions. Results on three phase unbalanced IEEE 37 and IEEE 123 bus test systems reveal the superior performance of the proposed Bayesian framework. The computational complexity for the proposed Bayesian framework is also quantified.

42 ENGINEERING↗
DOE OSTI2025

FedCSpc: A Cross-Silo Federated Learning System With Error-Bounded Lossy Parameter Compression

Cross-Silo federated learning is widely used for scaling deep neural network (DNN) training over data silos from different locations worldwide while guaranteeing data privacy. Communication has been identified as the main bottleneck when training large-scale models due to large-volume model parameters and gradient transmission across public networks with limited bandwidth. Most previous works focus on gradient compression, while limited work tries to compress parameters that can not be ignored and extremely affect communication performance during the training. Here, to bridge this gap, we propose FedCSpc: an efficient cross-silo federated learning system with an XAI-driven adaptive parameter compression strategy for large-scale model training. Our work substantially differs from existing gradient compression techniques due to the distinct data features of gradient and parameter. The key contributions of this paper are fourfold. (1) Our designed FedCSpc proposes to compress the parameter during the training using the state-of-the-art error-bounded lossy compressor – SZ3. (2) We develop an adaptive compression error bound adjustment algorithm to guarantee the model accuracy effectively. (3) We exploit an efficient approach to utilize the idle CPU resources of clients to compress the parameters. (4) We perform a comprehensive evaluation with a wide range of models and benchmarks on a GPU cluster with 65 GPUs. Results show that FedCSpc can achieve the same model accuracy as FedAvg while reducing the data volume of parameters and gradients in communication by up to 7.39× and 288×, respectively. With 32 clients on a 4 Gb size model, FedCSpc significantly outperforms FedAvg in wall-clock time in the emulated WAN environment (at the bandwidth of 1 Gbps or lower without loss of generality).

SZ3↗
DOE OSTI2025

Electroplated Polymer‐Modified Carbon Fiber for Performance‐Enhancing Composite Interfaces

Carbon fiber composite performance relies on the fiber-matrix interface for effective load transfer. To enhance interfacial properties between the fiber and matrix, often carbon fiber surfaces are oxidatively and covalently modified to incorporate chemical functional groups. By contrast, here, noncovalent electrodeposition of functional polyelectrolyte is applied onto conducting carbon fibers from aqueous solutions. A natural polymer, chitosan (CS), is electro-deposited onto the fiber surface, which undergoes multi-scale physical interactions. The bound CS layer with abundant amine functionalities reacts with epoxy moieties within the matrix to improve the interfacial properties. The scalable and energy-efficient electrodeposition eliminates traditional functionalization and sizing requirements of carbon fiber while delivering significantly higher mechanical performance with enhanced consistency. For continuous fiber reinforced composites, compared to conventional fibers, apparent interlaminar shear strength increases by 27%, reaching ≈86 MPa. The short fiber composites with only 2–11 wt.% fibers exhibit ≈20% increase in tensile strength with a peak performance of 120 MPa. Unlike traditionally treated and sized carbon fiber, this approach delivers coated fibers with long shelf-life and allows recovery of both CS in electrolyte form and carbon fiber by continuous electrochemical processing of the modified fibers with inverse polarity; thus, it promotes overall fiber recyclability.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Supernova Muons: New Constraints on Z' Bosons, Axions and ALPs

New light particles produced in supernovae can lead to additional energy loss and a consequent deficit in neutrino production in conflict with the neutrinos observed from Supernova 1987A (SN1987A). Contrary to the majority of previous SN1987A studies, we examine the impact of Z' bosons, axions, and axion-like particles (ALPs) interacting with the muons produced in SN1987A. For the first time, we find constraints on generic Z' bosons coupled to muons, and apply our results to particle models including gauged $L_μ –L_τ$ number, U(1)$_{L_μ–L_τ}$, and gauged B – L number, U(1)$_{B–L}$. We constrain Z' bosons with masses up to about 250 – 500 MeV, and down to about 10 –9 in Z'-muon coupling. We also extend previous work on axion-muon couplings by examining the importance of loop-level interactions, as well as performing calculations over a wider range of axion masses. We constrain muon-coupled axions from arbitrarily low masses up to about 200 – 500 MeV, with bounds extending down to axion-muon couplings of approximately 10 –8 GeV –1 . We conclude that supernovae broadly provide a sensitive probe of new lightly-coupled particles interacting with muons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Chloride, Alkoxide, or Silicon: The Bridging Ligand Dictates the Spin State in Dicobalt Expanded Pincer Complexes

We report the synthesis and characterization of a series of high- and low-spin dicobalt complexes of the tBu PNNP expanded pincer ligand. Reacting this dinucleating ligand in its neutral form with two equiv of CoCl 2 (tetrahydrofuran) 1.5 yields a high-spin dicobalt complex featuring one Co inside and one Co outside of the dinucleating pocket. Performing the same reaction in the presence of two equivalents of KOtBu provides access to a high-spin dicobalt complex wherein both Co centers are bound within the PNNP pocket, and this complex also features a bridging OtBu ligand. Reacting either of the high-spin complexes with excess diethyl silane affords a low-spin dicobalt complex containing two unusual bridging Si-based ligands. These complexes were investigated using NMR spectroscopy, XAS, single crystal X-ray structure determination, and computational methods, showing that the Si-based ligands are best described as base-stabilized silylenes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Predicting the von Neumann entanglement entropy using a graph neural network

Calculating the von Neumann entanglement entropy from experimental data is challenging due to its dependence on the complete wavefunction, forcing reliance on approximations such as classical mutual information (MI). We propose a machine learning approach using a graph neural network to predict the von Neumann entropy directly from experimentally accessible bitstrings. We test this approach on a Rydberg ladder system and achieve a mean absolute error of $3.6\,\times 10^{-3}$ when evaluating within the training range on a dataset with entropy values ranging from 0 to 1.9. The model achieves a mean absolute percentage error of 1.44% and outperforms MI-based bounds. When tested beyond the training range, the model maintains reasonable accuracy. Furthermore, we demonstrate that fine-tuning the model with small datasets significantly improves performance on data outside the original training range.

graph neural networks↗
DOE OSTI2021

Orbital Conflict: Cutting Planes for Symmetric Integer Programs

Cutting planes have been an important factor in the impressive progress made by integer programming (IP) solvers in the past two decades. However, cutting planes have had little impact on improving performance for symmetric IPs. Rather, the main breakthroughs for solving symmetric IPs have been achieved by cleverly exploiting symmetry in the enumeration phase of branch and bound. In this work, we introduce a hierarchy of cutting planes that arise from a reinterpretation of symmetry-exploiting branching methods. There are too many inequalities in the hierarchy to be used efficiently in a direct manner. However, the lowest levels of this cutting-plane hierarchy can be implicitly exploited by enhancing the conflict graph of the integer programming instance and by generating inequalities such as clique cuts valid for the stable set relaxation of the instance. We provide computational evidence that the resulting symmetry-powered clique cuts can improve state-of-the-art symmetry-exploiting methods. Furthermore, the inequalities are then employed in a two-phase approach with high-throughput computations to solve heretofore unsolved symmetric integer programs arising from covering designs, establishing for the first time the covering radii of two binary-ternary codes.

97 MATHEMATICS AND COMPUTING↗