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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma

Purpose: To predict the Fuhrman grade of clear cell renal cell carcinoma (ccRCC) with a machine learning classifier based on single- or three-phase computed tomography (CT) images. Materials and methods: Patients with pathologically proven ccRCC from February 1, 2009 to September 31, 2018 who were not treated were retrospectively collected for machine learning-based analysis. The texture features were extracted and ranked from precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP) and three-phase CT images, and open-source gradient boosting from the decision tree library of CatBoost was used to establish a machine learning classifier to differentiate low- from high-grade ccRCC. The performances of machine learning classifiers based on features from single- and three-phase CT images were compared with each other. Results: A total of 231 patients with 232 pathologically proven ccRCC lesions were retrospectively collected. 35, 36, 41, and 22 Features were extracted and ranked from PCP, CMP, NP, and three-phase CT images, respectively. The machine learning model based on three-phase CT images [area under the ROC curve (AUC) = 0.87] achieved the best diagnostic performance for differentiating low- from high-grade ccRCC, followed by single-phase NP (AUC = 0.84), CMP (AUC = 0.80), and PCP images (AUC = 0.82). Conclusion: Machine learning classifiers can be promising noninvasive techniques to differentiate low- and high-Fuhrman nuclear grade ccRCC, and classifiers based on three-phase CT images are superior to those based on features from each single phase.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Exploiting Machine Learning in Multiscale Modelling of Materials

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.

42 ENGINEERING↗
DOE OSTI2018

Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics

Machine learning has been effective at detecting patterns and predicting the response of systems that behave free of natural laws. Examples include learning crowd dynamics, recommender systems and autonomous mobility. There also have been applications to the search for new materials that draw upon big-data classification problems. Though, when it comes to physical systems governed by conservation laws, the role of machine learning has been more limited. Here, we present our recent work in exploring the role of machine learning methods in discovering, or aiding, the search for physics. Specifically, we focus on using machine learning algorithms to represent high-dimensional free energy surfaces with the goal of identifying precipitate morphologies in alloy systems. Traditionally, this problem has been approached by combining phase field models, which impose first-order dynamics, with elasticity, to traverse a free energy landscape in search of minima. Equilibrium precipitate morphologies occur at these minima. Here, we exploit the machine learning methods to represent high-dimensional data, combined with surrogate optimization, sensitivity analysis and multifidelity modeling as an alternate framework to explore phenomena controlled by energy extremization. This combination of data-driven methods offers an alternative to the imposition of first-order dynamics via phase field methods, and represents one aspect of applying machine learning to materials physics.

42 ENGINEERING↗
DOE OSTI2024

Recycling of CF-ABS machining waste for large format additive manufacturing

Large format additive manufacturing (LFAM) necessitates the use of short fiber thermoplastic composites, such as carbon fiber filled acrylonitrile butadiene styrene, to enable printing. Currently, when LFAM parts are machined into their final shape, the machining scrap (i.e., small flake like particles and offcuts) is landfilled. Previous studies have demonstrated the viability of recycling end-of-life LFAM parts by shredding and optionally re-compounding the material back into pellets. However, there is little understanding of the value and performance of recycled material made from LFAM machining scrap, which if pursued could motivate more broad recycling of this waste stream. In this study, recycled in-process machining scrap is explored as an LFAM feedstock source. Herein, it is found that the primary degradation mechanism of the recycled material is significant fiber length attrition during surface machining. While this fiber attrition negatively impacts the mechanical performance of the material in the print direction, it seems that the changes in processing behaviors and print quality, namely the surface roughness of the printed structure associated with shorter fiber lengths, is beneficial to interlayer adhesion. The tensile strength and elastic modulus of the recycled material, in the print direction, decreased 11% and 31% respectively compared to the pristine material. However, in the layer-wise direction it was found that the recycled material exhibited no significant change in elastic modulus and a significant 21% increase in tensile strength – a surprising result. In conclusion, this work indicates that machining waste could be a viable material stream for recycled LFAM feedstock materials.

42 ENGINEERING↗
DOE OSTI2026

Subseasonal Forecasting and MJO Teleconnections in Machine Learning Weather Prediction Models

Abstract In recent years, machine‐learning (ML) models trained on reanalysis data have rivaled physics‐based forecast models in terms of performance skill for global weather forecasting. With increased rollout stability, the question of how these models perform for subseasonal to seasonal (S2S, week 3–8) forecasting has emerged. In this study we run a large set of subseasonal hindcasts over 2004–2023 to evaluate two ML weather forecast models at the S2S time scale, SFNO‐HENS (Nvidia, fully ML) and NeuralGCM (Google Research, hybrid). Corresponding hindcasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) are used as a baseline for comparison to a physics‐based model. Because our focus is on predicting moisture transport over the Western United States between October and March, we evaluate the models' prediction skill for the Madden‐Julian Oscillation (MJO) and its associated teleconnections in the North Pacific. We find that both ML models are competitive with the ECWMF model, with comparable skill in predicting the North Pacific large‐scale circulation and the MJO at week 3 and beyond. Even though overall the mid‐latitude subseasonal prediction skill remains low, the ML models exhibit interesting behavior such as a realistic propagation of the MJO across the Maritime Continent and realistic teleconnections. A SFNO‐HENS sensitivity experiment with altered initial conditions in the tropics demonstrates the stability of the model, and it illustrates the capability of ML models to represent important physical processes of the atmosphere at the S2S time scale. Plain Language Summary Predicting weather patterns and precipitation a few weeks in advance (subseasonal time scale) is of great interest for stakeholders such as water managers in the Southwest United States (US), where arid conditions prevail. Subseasonal forecasts from traditional weather forecast models exhibit low skill in the region, limiting their applicability. Here we examine whether the recent breakthrough in weather forecasting made with machine learning/artificial intelligence models can translate to improved subseasonal forecasts. Recently‐developed machine learning models exhibit comparable skill to a state‐of‐the‐art physics‐based model for predicting weather patterns in the North Pacific/North America region, and associated moisture transport. The same applies to their skill in predicting the tropical pattern, the Madden‐Julian Oscillation, and its important remote perturbations over the midlatitude East Pacific and Southwest US. Additionally, a perturbation experiment carried out with one of the machine learning models illustrates their ability to not only predict the evolution of atmospheric fields, but also to learn and represent physical processes such as tropics‐extratropics Rossby wave propagation. Key Points Two machine learning weather forecast models exhibit state‐of‐the‐art prediction skill at the subseasonal time scale in the Pacific sector The models equal ECWMF in terms of Madden‐Julian oscillation (MJO) prediction skill, and they accurately predict the MJO propagation and associated teleconnections The two machine‐learning models represent key physical processes for subseasonal prediction, despite being trained for weather forecasting

Peings, Yannick↗
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↗