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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evaluating proxies for the drivers of natural gas productivity using machine-learning models

We report the extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning (ML) techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir productivity. We have used machine-learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included (1) gas production, (2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements, (3) microseismic attributes, (4) long-period long-duration (LPLD) event counts, (5) fracture counts, and (6) stimulation parameters that included the fluid injection volume and average pumping pressure. To identify observable proxies for the drivers of gas production, we evaluated five commonly used ML approaches including multivariate adaptive regression spline, Gaussian mixture model, random forest, gradient boosting, and neural network. We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs. Plain language summary: We use machine-learning methods and data-driven selection of reservoir parameters to rank and better understand their importance in determining gas production, which can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.

58 GEOSCIENCES↗
DOE OSTI2022

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.

Rao, Ashwin P. (ORCID:0000000319312568)↗
DOE OSTI2023

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor’s sensitivity (EC 50 ) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC 50 . Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A data ecosystem to support machine learning in materials science

Facilitating the application of machine learning to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materialsspecific machine learning models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with machine learning models and how users can access those capabilities through web and programmatic interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
DOE OSTI2025

Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning

Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.

58 GEOSCIENCES↗
DOE OSTI2021

Superconducting Radio-frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory

We report on the development of machine learning models for classifying C100 superconducting radiofrequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time - rather than postmortem - identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques and present preliminary work on cavity and fault prediction using data collected prior to a failure event.

Tennant, C. D.↗
DOE OSTI2014

Fundamental studies on initiation and evolution of multi-channel discharges and their application to next generation pulsed power machines.

Future pulsed power systems may rely on linear transformer driver (LTD) technology. The LTD's will be the building blocks for a driver that can deliver higher current than the Z-Machine. The LTD's would require tens of thousands of low inductance ( %3C 85nH), high voltage (200 kV DC) switches with high reliability and long lifetime ( 10 4 shots). Sandia's Z-Machine employs 36 megavolt class switches that are laser triggered by a single channel discharge. This is feasible for tens of switches but the high inductance and short switch life- time associated with the single channel discharge are undesirable for future machines. Thus the fundamental problem is how to lower inductance and losses while increasing switch life- time and reliability. These goals can be achieved by increasing the number of current-carrying channels. The rail gap switch is ideal for this purpose. Although those switches have been extensively studied during the past decades, each effort has only characterized a particular switch. There is no comprehensive understanding of the underlying physics that would allow predictive capability for arbitrary switch geometry. We have studied rail gap switches via an extensive suite of advanced diagnostics in synergy with theoretical physics and advanced modeling capability. Design and topology of multichannel switches as they relate to discharge dynamics are investigated. This involves electrically and optically triggered rail gaps, as well as discrete multi-site switch concepts.

Schwarz, Jens↗
DOE OSTI2015

Characterization of Contact and Bulk Thermal Resistance of Laminations for Electric Machines

The ability to remove heat from an electric machine depends on the passive stack thermal resistances within the machine and the convective cooling performance of the selected cooling technology. This report focuses on the passive thermal design, specifically properties of the stator and rotor lamination stacks. Orthotropic thermal conductivity, specific heat, and density are reported. Four materials commonly used in electric machines were tested, including M19 (29 and 26 gauge), HF10, and Arnon 7 materials.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Joint FAM/Line Management Assessment Report on LLNL Machine Guarding Safety Program

The LLNL Safety Program for Machine Guarding is implemented to comply with requirements in the ES&H Manual Document 11.2, "Hazards-General and Miscellaneous," Section 13 Machine Guarding (Rev 18, issued Dec. 15, 2015). The primary goal of this LLNL Safety Program is to ensure that LLNL operations involving machine guarding are managed so that workers, equipment and government property are adequately protected. This means that all such operations are planned and approved using the Integrated Safety Management System to provide the most cost effective and safest means available to support the LLNL mission.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Quantifying Uncertainty to Improve Decision Making in Machine Learning

Data-driven modeling, including machine learning methods, continue to play an increasing role in society. Data-driven methods impact decision making for applications ranging from everyday determinations about which news people see and control of self-driving cars to high-consequence national security situations related to cyber security and analysis of nuclear weapons reliability. Although modern machine learning methods have made great strides in model induction and show excellent performance in a broad variety of complex domains, uncertainty remains an inherent aspect of any data-driven model. In this report, we provide an update to the preliminary results on uncertainty quantification for machine learning presented in SAND2017-6776. Specifically, we improve upon the general problem definition and expand upon the experiments conducted for the earlier re- port. Most importantly, we summarize key lessons learned about how and when uncertainty quantification can inform decision making and provide valuable insights into the quality of learned models and potential improvements to them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Considerations for NSLS-II Synchrotron Radiation Protection When Operating Damping Wigglers at Low Machine Energy

The NSLS-II storage ring vacuum chamber, including frontends (FE) and beamlines (BL), is protected from possible damage from synchrotron radiation (SR) emitted from insertion devices (IDs) by a dedicated active interlock system (AIS). The system monitors electron beam position and angle and triggers a beam dump if the beam orbit is outside of the active interlock envelope (AIE). The AIE was calculated under the assumptions of 3 GeV beam energy and ID gaps set to their minimum operating values (i.e. “fully closed”). Recently it was proposed to perform machine studies that would ramp the stored beam energy significantly below the nominal operational value of 3 GeV. These studies may potentially include the use of NSLS-II damping wigglers (DWs) for electron beam emittance reduction and control. These DWs have the minimum operating gap of g min =15 mm with the corresponding peak magnetic field B 0 =1.8 T. During regular machine operations or studies at 3 GeV, DWs are usually held at this minimum gap (thus maximum field), or, alternatively, they are fully opened to the gap of 150 mm, which corresponds to a negligible residual magnetic field. However, any intermediate values of wiggler gap can be easily set through the control system with micron level precision, so wiggler fields in the range of 0< B < B 0 can be precisely set and maintained. For the low energy studies two likely modes of DW operation must be considered: 1) the maximum allowable wiggler filed is proportional to the storage ring energy, B ( E )= B 0 E /E 0 ; and 2) the maximum allowable wiggler field (and thus the minimum allowable gap) is energy independent and is equal to its nominal 3 GeV value of 1.8 T (i.e. wiggler gap is fixed at 15 mm). The aim of this technical note is to consider the synchrotron radiation protection (SRP) implications of DW use during low energy machine studies, when DWs are operated in either of these two modes. For consistency with earlier SRP documents we will quote all the powers and power densities at the nominal NSLS-II design beam current of 500 mA, even though the low energy studies will be done at significantly lower beam currents, hence the SR power will be proportionally smaller.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

Within the DOE complex, 3013 canisters are used to store Pu-bearing waste. Wall penetration due to corrosion, specifically stress corrosion cracking, is considered to be the most likely cause of failure over the 50-year lifetime of the canisters. This project has 2 objectives: The first is the development of machine learning algorithms to identify the presence of corrosion from a very large set of images generated by laser confocal microscope scanning of 3013 canister used to store Pu oxides. This portion of the LDRD constitutes image analysis of a metal surface for the presence of corrosion. The image processing algorithms developed for this project will provide a suitable basis for analysis of other types of corrosion data, which can be produced in vast quantities using modern devices. The second component of the LDRD consists of the development of machine learning algorithms that obtain molecular mechanics force-fields from ab-initio Density Functional Theory (DFT) calculations for corrosive attack by chlorides on 304L or 316L stainless steel. The goal of this latter component is to determine means for mitigating corrosion on a fundamental level, including coatings, welding methods, metal composition, etc. Force-field modeling is necessary for this endeavor because the incipience and progression of corrosion is the governed by molecular structures, grain boundaries, dislocations and surface structures represented by large numbers of atoms. Although DFT calculations are extremely adept at describing molecular scale processes, they are computationally expensive making them ill-suited for calculations of more than several hundred atoms, especially for the repeated applications inherent in material design. Fortunately, force-field methods provide an avenue for viable molecular-scale calculations involving the numbers of atoms involved in corrosion processes. The accuracy of the force-field calculations depends strongly on the accuracy of the force-field model, which is extremely difficult and time-consuming to derive from either data or ab-initio calculations. The use of machine learning algorithms has the potential to make the calculation of force-fields much more efficient but has not been explored significantly for corrosion processes. If shown to be viable, the technique would have wide -ranging impact on design of corrosion resistant materials and on the mitigation of corrosion in existing process systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Advancing Fusion with Machine Learning Research Needs Workshop

The pursuit of fusion energy has required extensive experimental and theoretical science activities to develop the knowledge needed that will enable design of successful fusion power plants. Even today, following decades of research in many key areas including plasma physics and material science, much remains to be learned to enable optimization of the tokamak or other paths to fusion energy. Data science methods from the fields of machine learning and artificial intelligence (ML/AI) offer opportunities for enabling or accelerating progress toward the realization of fusion energy by maximizing the amount and usefulness of information extracted from experimental and simulation output data. Jointly supported by the Department of Energy Offices of Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR), a workshop was organized to identify Priority Research Opportunities (PRO’s) for application of ML/AI methods to enable accelerated solution of fusion problems. The resulting “Advancing Fusion with Machine Learning Research Needs Workshop,” held in Gaithersburg, MD, April 30 – May 2, 2019, brought together ~ 60 experts in fields spanning fusion science, data science, statistical inference and mathematics, machine learning, and artificial intelligence, along with DOE program managers and technical experts, to identify key PRO’s.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Report of the Workshop on Advanced Fusion with Machine Learning, April 30-May 2, 2019

The pursuit of fusion energy has led to extensive activities in both experimental and theoretical science. The central goal of all these activities has been to develop the knowledge necessary for the design of successful fusion power plants. Even today, following decades of research in many key areas including plasma physics and material science, much remains to be learned in order to achieve optimization of the tokamak or other paths to fusion energy. Data science methods from the fields of machine learning and artificial intelligence (ML/AI) offer opportunities for enabling or accelerating progress toward the realization of fusion energy by maximizing the amount and usefulness of information extracted from experimental and simulation output data. From April 30 to May 2 of 2019, a workshop on “Advancing Fusion with Machine Learning Research Needs” was held in Gaithersburg, MD, with joint support from the Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR) Programs. The workshop brought together ~ 60 experts in fields spanning fusion science, data science, statistical inference and mathematics, machine learning, and artificial intelligence, along with DOE program managers and technical experts, with the goal of identifying Priority Research Opportunities (PROs) for the application of ML/AI methods to accelerating progress on solving fusion problems. During the workshop, seven key PROs were identified. The description of each PRO includes relevant fusion problems, potentially useful ML/AI approaches, known gaps currently preventing the use of such approaches, and research guidelines to maximize the effective application of ML/AI methods to the PRO. The report containing the full description of the workshop outcomes is available at https://science.osti.gov/-/media/fes/pdf/workshopreports/FES_ASCR_Machine_Learning_Report.pdf

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Dual Phase Soft Magnetic Laminates for Low-cost, Non/Reduced-Rare-Earth Containing Electrical Machines

To accelerate the mass market adoption of electric drive vehicles, the key technology barriers in electric motors are (1) magnet cost and rare-earth element price volatility; (2) non-rare-earth electric motor performance; and (3) materials property optimization. The goal of this project was to address these barriers by advancing a unique and innovative dual phase soft magnetic material technology and demonstrating the material in a 30-kW synchronous reluctance motor without using any permanent magnet for electric vehicles. Dual phase magnetic materials offer the electric motor designer the ability to locally control the magnetic saturation level in a motor laminate, while at the same time enhancing the mechanical strength of the laminate material, resulting in an enhancement in motor performance and efficiency. Scalable dual phase soft magnetic laminates manufacturing technologies were developed in collaboration with multiple US manufacturers. 1000 lbs of alloy sheet with a thickness of 0.25mm and width of 280 mm was manufactured within the specifications. Batch sizes of up to 240 laminates per run were produced from the alloy sheet. Two prototype motors with dual phase soft magnetic laminates were designed, built, and tested. The major goal of building the subscale prototype as a pathway to develop scalable manufacturing technologies for the dual phase soft magnetic laminates was met. The additional goal of building and testing the subscale prototype in order to validate the calculated performance with the tested motor performance was also met. For the full-scale 30kW continuous power synchronous reluctance motor prototype, the tested performance met the targets in terms of continuous power at the operating speeds up to 8000 rpm. Post-test studies were conducted and the root causes for the discrepancy between the predicted and tested peak power, continuous power at high speed range, and efficiency were identified. Further modeling study showed that the dual phase rotor machine has a 27% higher torque to active weight ratio than an equivalent performance silicon steel rotor machine. Application space and multiple discussions with traction motor and electric vehicle manufacturers for commercialization of the dual phase soft magnetic material technology were identified and conducted. An initial cost model was established based on the developed manufacturing technologies with the US manufacturers. Future paths for further cost reduction were identified, including increasing the market volume by broadening the applications of the dual phase soft magnetic laminate technology for electric machines in other energy sections such as oil & gas, heating, ventilation, and air conditioning (HVAC), and power generation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Parsimonious Inference Information-Theoretic Foundations for a Complete Theory of Machine Learning (CIS-LDRD Project 218313 Final Technical Report)

This work examines how we may cast machine learning within a complete Bayesian framework to quantify and suppress explanatory complexity from first principles. Our investigation into both the philosophy and mathematics of rational belief leads us to emphasize the critical role of Bayesian inference in learning well-justified predictions within a rigorous and complete extended logic. The Bayesian framework allows us to coherently account for evidence in the learned plausibility of potential explanations. As an extended logic, the Bayesian paradigm regards probability as a notion of degrees of truth. In order to satisfy critical properties of probability as a coherent measure, as well as maintain consistency with binary propositional logic, we arrive at Bayes' Theorem as the only justifiable mechanism to update our beliefs to account for empiracle evidence. Yet, in the machine learning paradigm, where explanations are unconstrained algorithmic abstractions, we arrive at a critical challenge: Bayesian inference requires prior belief. Conventional approaches fail to yield a consistent framework in which we could compare prior plausibility among the infinities of potential choices in learning architectures. The difficulty of articulating well-justified prior belief over abstract models is the provinence of memorization in traditional machine learning training practices. This becomes exceptionally problematic in the context of limited datasets, when we wish to learn justifiable predictions from only a small amount of data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

The development of algorithms for machine learning and data analysis for the 3013 MIS corrosion surveillance program is a collaborative effort by SRNL, USC and GT. For corrosion detection, LCM image data is extracted from large binary files, with software written to convert the data to physical attributes (i.e. height, color and grayscale values; all as functions of a location in a plane projection). The user interface for the software permits selective downloading of binary data and interrogation of attributes. User input thresholds are used to flag attributes of interest. Machine learning algorithms, developed for this application, are used to determine whether the features are the result of corrosion. To address the fundamental mechanisms of corrosion, machine learning algorithms are being developed to derive interatomic potential force-fields from ab-initio DFT calculations. The goal is to apply molecular modeling on a large enough scale to guide the design of resistant materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗