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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Decoding defect statistics from diffractograms via machine learning

Abstract Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Diagnostics for machine protection of DEMO

DEMO aims to (i) integrate, demonstrate and validate all relevant technology necessary to convert fusion energy to electrical energy and (ii) that the machine and its operations are economically and environmentally acceptable. To maintain the efficiency and availability of the machine, there are several physics and combined physics/technology issues as well as the engineering issues. Machine Protection (also known as Protection of Investment) addresses both the risks to plant (to avoid costly repair or replacement) and the risks to normal operating time (to avoid loss of productivity and the return on investment). The plasma-related Machine Protection issues involve measurement and control of plasma stability, plasma purity, and plasma-wall interactions. Machine Protection aims to avoid hitting catastrophic limits by using early warning alarm systems, and controlled termination or avoidance, involving coordinated actions of the magnets, gas and auxiliary heating or current-drive systems. This article outlines the key processes, some of which are used in present-day tokamaks and some of which are new specifically for DEMO (e.g. First wall and divertor power handling) and reveals the need to research and develop new science and technology for Machine Protections in DEMO's high radiation and thermal fields. This work was funded by the RCUK Energy Programme under grant EP/I501045 and the European Communities under the contract of Association between EURATOM and CCFE and conducted partly under EFDA PPPT (WP13-DAS04). The views and opinions expressed herein do not necessarily reflect those of the European Commission.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Acceleration of saddle-point searches with machine learning

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

A multi-machine scaling of halo current rotation

Halo currents generated during unmitigated tokamak disruptions are known to develop rotating asymmetric features that are of great concern to ITER because they can dynamically amplify the mechanical stresses on the machine. This paper presents a multi-machine analysis of these phenomena. More specifically, data from C-Mod, NSTX, ASDEX Upgrade, DIII-D, and JET are used to develop empirical scalings of three key quantities: the machine-specific minimum current quench time, $ \newcommand{\tauCQ}{\tau_{{\rm CQ}}} \tauCQ$; the halo current rotation duration, $ \newcommand{\trot}{t_{{\rm rot}}} \newcommand{\tr}{{\rm tr}} \trot$ ; and (3) the average halo current rotation frequency, $ \newcommand{\favg}{\langle f_{{\rm h}} \rangle} \favg$ . These data reveal that the normalized rotation duration, $ \newcommand{\tauCQ}{\tau_{{\rm CQ}}} \newcommand{\trot}{t_{{\rm rot}}} \newcommand{\tr}{{\rm tr}} \trot/\tauCQ$ , and the average rotation velocity, $ \newcommand{\vavg}{\langle {v}_{{\rm h}} \rangle} \vavg$ , are surprisingly consistent from machine to machine. Furthermore, comparisons between carbon and metal wall machines show that metal walls have minimal impact on the behavior of rotating halo currents. Finally, upon projecting to ITER, the empirical scalings indicate that substantial halo current rotation above $ \newcommand{\favg}{\langle \,f_{{\rm h}} \rangle} \favg=20$ Hz is to be expected. More importantly, depending on the projected value of $ \newcommand{\tauCQ}{\tau_{{\rm CQ}}} \tauCQ$ in ITER, substantial rotation could also occur in the resonant frequency range of 6–20 Hz. In conclusion, the possibility of damaging halo current rotation during unmitigated disruptions in ITER cannot be ruled out.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Rotor Magnet Fault Analysis in Permanent Magnet AC Machines Under Load Conditions - All Electric Transportation Systems

This paper studies the performance of a Permanent Magnet (PM) alternating-current (AC) machine when subjected to faults under load conditions. The effect of varying load torques, and unbalances are found to be potential causes of rotor faults, inducing physical magnet defects or broken rotor-PMs. The consequent and immediate impact on the machine quantities is observed through anomalous change in torque-speed characteristics and other vital signatures such as machine back-EMF/flux and motor current signatures (MCS). The present research study develops and illustrates a method to diagnose physical magnet defects (fault) in PMAC machines by estimating the machine torque/back-EMF constant, Ke. The constant Ke is a measure of magnet strength exhibiting the health of rotor PMs, indicative of faults. Advanced research studies and investigations are carried out to establish motor approximations and signature-based analysis as significant viable tools for diagnosing machine faults even under load conditions.

alternating current↗
DOE OSTI2023

A Comparison of Outer Rotor Radial and Axial Flux Machines for Application in Electric Vehicles

Axial flux machines have attracted a lot of interest in the recent years as potential high torque low weight candidates for use as traction motors in electric vehicles (EV). This paper compares axial and radial flux machines for electric vehicle applications. An external rotor radial flux machine with a Halbach array surface permanent magnet rotor and concentrated windings is chosen as a baseline to compare with axial flux designs. Both axial and radial flux motors are sized to meet the EV same requirements. Multi-objective design optimization using differential evolution minimizing loss and volume is carried out for both types of machines. Hundreds of candidate designs for each type of machine are analyzed, pareto fronts are identified and compared. The potential advantages of axial flux machines are evaluated and quantified.

Rallabandi, Vandana↗
DOE OSTI2017

Stability Assessment of a System Comprising a Single Machine and Inverter with Scalable Ratings

Synchronous machines have traditionally acted as the foundation of large-scale electrical infrastructures and their physical properties have formed the cornerstone of system operations. However, with the increased integration of distributed renewable resources and energy-storage technologies, there is a need to systematically acknowledge the dynamics of power-electronics inverters - the primary energy-conversion interface in such systems - in all aspects of modeling, analysis, and control of the bulk power network. In this paper, we assess the properties of coupled machine-inverter systems by studying an elementary system comprised of a synchronous generator, three-phase inverter, and a load. The inverter model is formulated such that its power rating can be scaled continuously across power levels while preserving its closed-loop response. Accordingly, the properties of the machine-inverter system can be assessed for varying ratios of machine-to-inverter power ratings. After linearizing the model and assessing its eigenvalues, we show that system stability is highly dependent on the inverter current controller and machine exciter, thus uncovering a key concern with mixed machine-inverter systems and motivating the need for next-generation grid-stabilizing inverter controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Comparison of Doubly-Fed Induction Generator Machine Models for Real-Time Simulations

This work presents a detailed comparison among three implementations of the Doubly-Fed Induction Generator (DFIG) machine model. The alpha-beta-gamma, synchronous (dqo), and natural frame dynamic machine models were deployed on a Field-Programmable Gate Array (FPGA) and compared in terms of execution time and resource utilization. These machine models were also validated against a physical DFIG machine during balanced and unbalanced grid conditions. A benchmark is presented with the objective of selecting the machine model, orientation and state-space representation that is best suited for real-time simulations. The selected machine model is used to develop a real-time emulator of a Type-3 wind system. This emulator enables the study of wind turbine system in realtime using a validated platform while allowing controller testing without relying on expensive third-party solutions.

Ferrari Maglia, Max↗
DOE OSTI2014

SU-E-T-113: Dose Distribution Using Respiratory Signals and Machine Parameters During Treatment

Purpose: Volumetric modulated arc therapy (VMAT) is a rotational intensity-modulated radiotherapy (IMRT) technique capable of acquiring projection images during treatment. Treatment plans for lung tumors using stereotactic body radiotherapy (SBRT) are calculated with planning computed tomography (CT) images only exhale phase. Purpose of this study is to evaluate dose distribution by reconstructing from only the data such as respiratory signals and machine parameters acquired during treatment. Methods: Phantom and three patients with lung tumor underwent CT scans for treatment planning. They were treated by VMAT while acquiring projection images to derive their respiratory signals and machine parameters including positions of multi leaf collimators, dose rates and integrated monitor units. The respiratory signals were divided into 4 and 10 phases and machine parameters were correlated with the divided respiratory signals based on the gantry angle. Dose distributions of each respiratory phase were calculated from plans which were reconstructed from the respiratory signals and the machine parameters during treatment. The doses at isocenter, maximum point and the centroid of target were evaluated. Results and Discussion: Dose distributions during treatment were calculated using the machine parameters and the respiratory signals detected from projection images. Maximum dose difference between plan and in treatment distribution was −1.8±0.4% at centroid of target and dose differences of evaluated points between 4 and 10 phases were no significant. Conclusion: The present method successfully evaluated dose distribution using respiratory signals and machine parameters during treatment. This method is feasible to verify the actual dose for moving target.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-C-304-07: Are Small Field Detector Correction Factors Strongly Dependent On Machine-Specific Characteristics?

Purpose: The current small field dosimetry formalism utilizes quality correction factors to compensate for the difference in detector response relative to dose deposited in water. The correction factors are defined on a machine-specific basis for each beam quality and detector combination. Some research has suggested that the correction factors may only be weakly dependent on machine-to-machine variations, allowing for determinations of class-specific correction factors for various accelerator models. This research examines the differences in small field correction factors for three detectors across two Varian Truebeam accelerators to determine the correction factor dependence on machine-specific characteristics. Methods: Output factors were measured on two Varian Truebeam accelerators for equivalently tuned 6 MV and 6 FFF beams. Measurements were obtained using a commercial plastic scintillation detector (PSD), two ion chambers, and a diode detector. Measurements were made at a depth of 10 cm with an SSD of 100 cm for jaw-defined field sizes ranging from 3×3 cm{sup 2} to 0.6×0.6 cm{sup 2}, normalized to values at 5×5cm{sup 2}. Correction factors for each field on each machine were calculated as the ratio of the detector response to the PSD response. Percent change of correction factors for the chambers are presented relative to the primary machine. Results: The Exradin A26 demonstrates a difference of 9% for 6×6mm{sup 2} fields in both the 6FFF and 6MV beams. The A16 chamber demonstrates a 5%, and 3% difference in 6FFF and 6MV fields at the same field size respectively. The Edge diode exhibits less than 1.5% difference across both evaluated energies. Field sizes larger than 1.4×1.4cm2 demonstrated less than 1% difference for all detectors. Conclusion: Preliminary results suggest that class-specific correction may not be appropriate for micro-ionization chamber. For diode systems, the correction factor was substantially similar and may be useful for class-specific reference conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

SU-G-TeP4-09: Development of a Plan Data Aggregator for Time Efficient Physics Second-Checks of Machine Parameters for External Beam Treatment Plans

Purpose: Physics second-checks for external beam radiation therapy are performed, in-part, to verify that the machine parameters in the Record-and-Verify (R&V) system that will ultimately be sent to the LINAC exactly match the values initially calculated by the Treatment Planning System (TPS). While performing the second-check, a large portion of the physicists’ time is spent navigating and arranging display windows to locate and compare the relevant numerical values (MLC position, collimator rotation, field size, MU, etc.). Here, we describe the development of a software tool that guides the physicist by aggregating and succinctly displaying machine parameter data relevant to the physics second-check process. Methods: A data retrieval software tool was developed using Python to aggregate data and generate a list of machine parameters that are commonly verified during the physics second-check process. This software tool imported values from (i) the TPS RT Plan DICOM file and (ii) the MOSAIQ (R&V) Structured Query Language (SQL) database. The machine parameters aggregated for this study included: MLC positions, X&Y jaw positions, collimator rotation, gantry rotation, MU, dose rate, wedges and accessories, cumulative dose, energy, machine name, couch angle, and more. Results: A GUI interface was developed to generate a side-by-side display of the aggregated machine parameter values for each field, and presented to the physicist for direct visual comparison. This software tool was tested for 3D conformal, static IMRT, sliding window IMRT, and VMAT treatment plans. Conclusion: This software tool facilitated the data collection process needed in order for the physicist to conduct a second-check, thus yielding an optimized second-check workflow that was both more user friendly and time-efficient. Utilizing this software tool, the physicist was able to spend less time searching through the TPS PDF plan document and the R&V system and focus the second-check efforts on assessing the patient-specific plan-quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

A multi-machine scaling of halo current rotation

Halo currents generated during unmitigated tokamak disruptions are known to develop rotating asymmetric features that are of great concern to ITER because they can dynamically amplify the mechanical stresses on the machine. This paper presents a multi-machine analysis of these phenomena. More specifically, data from C-Mod, NSTX, ASDEX Upgrade, DIII-D, and JET are used to develop empirical scalings of three key quantities: (1) the machine-specific minimum current quench time, tauCQ; (2) the halo current rotation duration, trot; and (3) the average halo current rotation frequency, . These data reveal that the normalized rotation duration, trot/tauCQ, and the average rotation velocity, , are surprisingly consistent from machine to machine. Furthermore, comparisons between carbon and metal wall machines show that metal walls have minimal impact on the behavior of rotating halo currents. Finally, upon projecting to ITER, the empirical scalings indicate that substantial halo current rotation above = 20 Hz is to be expected. More importantly, depending on the projected value of tauCQ in ITER, substantial rotation could also occur in the resonant frequency range of 6-20 Hz. As such, the possibility of damaging halo current rotation during unmitigated disruptions in ITER cannot be ruled out.

Halo currents↗
DOE OSTI2023

State Machine Operation of Complex Systems

Operation of complex systems which depend on one or more other systems with many process variables often operate in more than one state. For each state there may be a variety of parameters of interest, and for each of these, one may require different alarm limits, different archiving needs, and have different critical parameters. Relying on operators to reliably change 10s-1000s of parameters for each system for each state is unreasonable. Not changing these parameters results in alarms being ignored or disabled, critical changes missed, and/or possible data archiving problems. To reliably manage the operation of complex systems, such as cryomodules (CMs), Fermilab is implementing state machines for each CM and an over-arching state machine for the PIP-II superconducting linac (SCL). The state machine transitions and operating parameters are stored/restored to/from a configuration database. Proper implementation of the state machines will not only ensure safe and reliable operation of the CMs, but will help ensure reliable data quality. A description of PIP-II SCL, details of the state machines, and lessons learned from limited use of the state machines in recent CM testing will be discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

Interactive Machine Learning in Data Exploitation

The goal of interactive machine learning is to help scientists and engineers exploit more of their specialized data in less time. Interactive machine learning focuses on methods that empower domain experts to control and direct machine learning tools from within the deployed environment, whereas traditional machine learning does this in the development environment. This difference allows interactive machine learning systems to be more responsive, more accurate and cheaper to develop and maintain. This article provides a basic introduction to the main components and tries to untangle the many ideas that must be combined to produce practical interactive learning systems. It also describes recent developments in machine learning that have significantly advanced the theoretical and practical foundations for the next generation of interactive tools.

Information Science↗
DOE OSTI2013

Verification of the Effectiveness of X-ray Machine Collimation

The Oak Ridge National Laboratory (ORNL) was tasked by the United States National Nuclear Security Administration’s Second Line of Defense (SLD) program with reducing the false alarm rate experienced by radiation portal monitors (RPM) installed near x-ray machines. Although the mechanism for interference between RPMs and x-ray machines is well understood, x-ray machines are often installed too near RPMs due to a variety of facility constraints. The result is underutilized RPMs that are plagued by a high false alarm rate from x-rays that escape the x-ray machine and are detected by the RPM. In this work, the effectiveness of an x-ray machine collimation system designed at ORNL is validated through the analysis of field data collected before and after the installation. The significant reduction in false alarm rate due to the collimation system is explicitly shown, and examples of field installations are provided. Utilization of the results presented here allows RPM systems to operate effectively even when installed near x-ray machines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Evaluation and Comparison of Machine Learning Techniques for Rapid QSTS Simulations

Rapid and accurate quasi-static time series (QSTS) analysis is becoming increasingly important for distribution system analysis as the complexity of the distribution system intensifies with the addition of new types, and quantities, of distributed energy resources (DER). The expanding need for hosting capacity analysis, control systems analysis, photovoltaic (PV) and DER impact analysis, and maintenance cost estimations are just a few reasons that QSTS is necessary. Historically, QSTS analysis has been prohibitively slow due to the number of computations required for a full-year analysis. Therefore, new techniques are required that allow QSTS analysis to rapidly be performed for many different use cases. This research demonstrates a novel approach to doing rapid QSTS analysis for analyzing the number of voltage regulator tap changes in a distribution system with PV components. A representative portion of a yearlong dataset is selected and QSTS analysis is performed to determine the number of tap changes, and this is used as training data for a machine learning algorithm. The machine learning algorithm is then used to predict the number of tap changes in the remaining portion of the year not analyzed directly with QSTS. The predictions from the machine learning algorithms are combined with the results of the partial year simulation for a final prediction for the entire year, with the goal of maintaining an error <10% on the full-year prediction. Five different machine learning techniques were evaluated and compared with each other; a neural network ensemble, a random forest decision tree ensemble, a boosted decision tree ensemble, support vector machines, and a convolutional neural network deep learning technique. A combination of the neural network ensemble together with the random forest produced the best results. Using 20% of the year as training data, analyzed with QSTS, the average performance of the technique resulted in ~2.5% error in the yearly tap changes, while maintaining a <10% 99.9 th percentile error bound on the results. This is a 5x speedup compared to a standard, full-length QSTS simulation. These results demonstrate the potential for applying machine learning techniques to facilitate modern distribution system analysis and further integration of distributed energy resources into the power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Decision Science for Machine Learning (DeSciML)

The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring rely on machine learning for aspects of their analyses. Likewise, future technologies, such as incorporation of data-driven methods into the stockpile surveillance and predictive failure analysis for weapons components, will all rely on decision-making that incorporates the output of machine learning models. In this project, our main focus was the development of decision scientific methods that combine uncertainty estimates for machine learning predictions, with a domain-specific model of error costs. Other focus areas include uncertainty measurement in ML predictions, designing decision rules using multiobjecive optimization, the value of uncertainty reduction, and decision-tailored uncertainty quantification for probability estimates. By laying foundations for rigorous decision making based on the predictions of machine learning models, these approaches are directly relevant to every national security mission that applies, or will apply, machine learning to data, most of which entail some decision context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.

Chan, Jay↗