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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

42 ENGINEERING↗
DOE OSTI2017

Graphics processing unit accelerated phase field dislocation dynamics: Application to bi-metallic interfaces

We present the first high-performance computing implementation of the meso-scale phase field dislocation dynamics (PFDD) model on a graphics processing unit (GPU)-based platform. The implementation takes advantage of the portable OpenACC standard directive pragmas along with Nvidia's compute unified device architecture (CUDA) fast Fourier transform (FFT) library called CUFFT to execute the FFT computations within the PFDD formulation on the same GPU platform. The overall implementation is termed ACCPFDD-CUFFT. The package is entirely performance portable due to the use of OPENACC-CUDA inter-operability, in which calls to CUDA functions are replaced with the OPENACC data regions for a host central processing unit (CPU) and device (GPU). A comprehensive benchmark study has been conducted, which compares a number of FFT routines, the Numerical Recipes FFT (FOURN), Fastest Fourier Transform in the West (FFTW), and the CUFFT. The last one exploits the advantages of the GPU hardware for FFT calculations. The novel ACCPFDD-CUFFT implementation is verified using the analytical solutions for the stress field around an infinite edge dislocation and subsequently applied to simulate the interaction and motion of dislocations through a bi-phase copper-nickel (Cu–Ni) interface. It is demonstrated that the ACCPFDD-CUFFT implementation on a single TESLA K80 GPU offers a 27.6X speedup relative to the serial version and a 5X speedup relative to the 22-multicore Intel Xeon CPU E5-2699 v4 @ 2.20 GHz version of the code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SPACE: 3D parallel solvers for Vlasov-Maxwell and Vlasov-Poisson equations for relativistic plasmas with atomic transformations

A parallel, relativistic, three-dimensional particle-in-cell code SPACE has been developed for the simulation of electromagnetic fields, relativistic particle beams, and plasmas. In addition to the standard second-order Particle-in-Cell (PIC) algorithm, SPACE includes efficient novel algorithms to resolve atomic physics processes such as multi-level ionization of plasma atoms, recombination, and electron attachment to dopants in dense neutral gases. SPACE also contains a highly adaptive particle-based method, called Adaptive Particle-in-Cloud (AP-Cloud), for solving the Vlasov-Poisson problems. It eliminates the traditional Cartesian mesh of PIC and replaces it with an adaptive octree data structure. The code's algorithms, structure, capabilities, parallelization strategy, and performance have been discussed. Additionally, typical examples of SPACE applications to accelerator science and engineering problems are described.

43 PARTICLE ACCELERATORS↗
DOE OSTI2014

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Unveiling X-ray absorption signatures of boron nitride via first-principles simulation and machine learning

Boron nitride (BN) allotropes hold great promise in many advanced applications ranging from optical and photonic devices to energy storage and battery systems to tribological components. The diverse functionalities of this material stem from BN’s highly tunable structural and electronic properties, which are governed by the versatile boron–nitrogen bonding configurations. Exploring the structural landscape of BN can unveil novel structures possessing unique properties suited for specific applications, therefore accelerating the design of next-generation advanced functional materials. In this work, we leverage boron K-edge X-ray absorption spectroscopy (XAS) as an effective probe for local structural features and chemical environments. A total of 210 BN crystal structures are generated via analogies to the extensive array of carbon allotropes, and XAS is simulated for each unique local motif within the resulting collection of structures. A mapping between structural features and spectral signatures was established by synergizing first-principle simulations with data-driven based post-analysis approaches. Specifically, we developed a neural network model that can satisfactorily predict spectra line shapes from local structural descriptors. Toward automatic spectroscopic interpretation of any new BN structures, supervised machine learning models, trained on this structure–spectrum dataset, can accurately infer local coordination environments from simulated XAS, highlighting the strength of this unique approach of combining high-fidelity first-principles simulation and machine-learning to accelerate target design of novel BN materials via rational understanding of local structure-spectrum correlations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.

Rettenberger, Luca↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
DOE OSTI2014

Atomistic study on dithiolated oligo-phenylenevinylene gated device

Thanks to their semiconducting behavior, conjugated molecules are considered as an attractive candidate for future electronic devices. Understanding the charge transport characteristics through such molecules for different device applications would accelerate the progress in the field of molecular electronics. In addition, it would become more feasible to introduce/enhance specific properties of molecular devices. This theoretical paper focuses on atomistic simulation and characterization of novel molecular FET employing dithiolated oligo-phenylenevinylene molecules. The simulation is validated by its agreement with the experimental measurements conducted on the same molecules. The employed molecule has oxygen linkers, which are responsible for the strongly nonlinear current characteristics on the molecular device. We perform a thorough atomistic device analysis to illustrate the principles behind the nonlinear current characteristics and the gating effect.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Uni-layer magnets: a new concept for LTS and HTS based superconducting magnets

A novel geometrical configuration to form a magnetic field perpendicular to an aperture, created by an asymmetric current distribution, within a single layer, and using a continuous ideal current line, named the uni-layer (UL) magnet, is here presented. The idea is compared to existing concepts in superconducting magnets, namely, the $\cos{\theta}$ sector magnet, stress managed $\cos{\theta}$ and canted $\cos{\theta}$. The UL magnet allows for a design with a continuous unit length (no layer jump), and an increased minimum bending radius of the conductor in relation to traditional $\cos{\theta}$ and canted $\cos{\theta}$ designs. Further, the specific characteristics of the UL design are especially advantageous for strain-sensitive and prone to winding degradation high-temperature superconductors, in very high field accelerator magnet applications, in which, high efficiency in the use of conductor, and a small aperture are required. The advantages with regard to the design and fabrication of UL magnets in relation to other concepts are also discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Overview of the Neutrinos from Stored Muons Facility - nuSTORM

Neutrino beams produced from the decay of muons in a racetrack-like decay ring (the so called Neutrino Factory) provide a powerful way to study neutrino oscillation physics and, in addition, provide unique beams for neutrino interaction studies. The Neutrinos from STORed Muons (nuSTORM) facility uses a neutrino factory-like design. Due to the particular nature of nuSTORM, it can also provide an intense, very pure, muon neutrino beam from pion decay. This so-called 'Neo-conventional' muon-neutrino beam from nuSTORM makes nuSTORM a hybrid neutrino factory. Here in this paper we describe the facility and give a detailed description of the neutrino beams that are available and the precision to which they can be characterized. We then show its potential for a neutrino interaction physics program and present sensitivity plots that indicate how well the facility can perform for short-baseline oscillation searches. Lastly, we comment on the performance potential of a 'Neo-conventional' muon neutrino beam optimized for long-baseline neutrino-oscillation physics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

A terahertz pump mega-electron-volt ultrafast electron diffraction probe apparatus at the SLAC Accelerator Structure Test Area facility

Here, we describe a new experimental setup capable of measuring structural dynamics following intense terahertz excitation. This system, developed at the SLAC Accelerator Structure Test Area facility, uses a high-energy ultrafast laser to produce intense terahertz pulses and femtosecond electron bunches that are accelerated to mega-electron-volt kinetic energies. The focused terahertz pulses have electric fields in excess of 600 kV/cm, and the resulting structural dynamics can be followed by electron diffraction. We also present some examples demonstrating its implementation where interactions between the THz pulses and the electron bunch are used to characterize the spatial and temporal characteristics of the THz field.

42 ENGINEERING↗
DOE OSTI2018

A mathematical model to systematically align superconducting radio frequency cavities employing the serial robot kinematic theory

In Superconducting Radio Frequency (SRF) cryomodules, precise alignment of cavities is necessary to ensure high beam quality and to increase the overall energetic efficiency. In the SSR1 (Single Spoke Resonator 1) cryomodule, the cavities are aligned using a system of seven screws which allows to control five Degree of Freedoms (DOFs). The relation between the rotation of each screw and the movement of the cavity is non-linear, thus the aligning process, currently based on a trial-and-error procedure, can be time consuming and affected by human error. In this paper the design of an alignment mathematical model, based on the serial robot kinematic, is described and tested on a SSR1 cavity. Furthermore, the goal of the proposed model is to give a step by step operational procedure that bring the SSR1 cavity to a desired location reducing the uncertainties related to the current operator-driven alignment process.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

An adaptive masking method for beam halo measurement by a digital micro mirror device

We report that in high intensity particle beams, irregularities in cathode emission, scattering effects, dark current and space charge forces can lead to a common phenomenon termed beam halo with associated emittance growth, beam loss and overall beam quality degradation. It is of fundamental interest and practical importance to precisely monitor such beam halo, which could offer valuable insights into the experimental strategy for the suppression of this undesired feature. A Digital Micro mirror Device (DMD) is a computer-controlled micro-electrical-mechanical system that contains a compact array of physically independent reflective mirrors. Using a standard DMD, we come up with an adaptive masking method for halo measurement, which demonstrates superior robustness and efficiency comparing to the conventional approaches as confirmed by simulation and experimental results. Our development includes an integrated package of hardware, software and a graphic user interface (GUI) that is robust and portable and can be applied as a beam halo diagnostic for electron guns or radiating charged particle beams in general.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

AI for Experimental Controls at Jefferson Lab

We report the AI for Experimental Controls project is developing an AI system to control and calibrate detector systems located at Jefferson Laboratory. Currently, calibrations are performed offline and require significant time and attention from experts. This work would reduce the amount of data and the amount of time spent calibrating in an offline setting. The first use case involves the Central Drift Chamber (CDC) located inside the GlueX spectrometer in Hall D. We use a combination of environmental and experimental data, such as atmospheric pressure, gas temperature, and the flux of incident particles as inputs to a sequential Neural Network (NN) to recommend a high voltage setting and the corresponding calibration constants in order to maintain consistent gain and optimal resolution throughout the experiment. Utilizing AI in this manner represents an initial shift from offline calibration towards near real time calibrations performed at Jefferson Laboratory.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Ultra-fast transverse beam orbit control in LCLS copper linac. Part I

We report current and future experiments at LCLS require x-ray pulse trains of variable time separation on the nanosecond scale. For instance, the cavity-based XFEL (CBXFEL) will use up to 4 pulses separated by 218.5 ns, the X-ray Laser Oscillator (XLO) will use 15 to 25 ns spaced pulses, and the Matter under Extreme Conditions (MEC) experiments use pulse trains separated by 5 nanoseconds or less. In this paper, we demonstrate an ultra-fast e-beam trajectory control method based on transverse electro-magnetic (TEM) striplines and state-of-the-art power sources, to enhance LCLS operations in these regimes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

The potential of a TeV-scale muon-ion collider

We propose the development of a novel muon-proton and muon-nucleus collider facility at the TeV scale that is capable of performing precision deep inelastic scattering measurements in new regimes and providing a rich program in nuclear and particle physics. Such a facility could seed, or leverage, the development of a muon-antimuon collider and make use of the existing hadron accelerator infrastructure when sited at a facility such as Brookhaven National Laboratory, Fermilab, or CERN. Here, we discuss the possible energy and luminosity design parameters for several collider configurations, and illustrate the science potential with several studies on deep inelastic scattering kinematics, Higgs and vector boson production, top quark production, and beyond Standard Model leptoquark production. Detector design considerations and a possible road map toward development are also given.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗