Kubo–Greenwood electrical conductivity formulation and implementation for projector augmented wave datasets
Not provided.
Computer Science↗
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Results for “Augmented”
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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Development of a Low-Level 39Ar Calibration Standard – Analysis by Absolute Gas Counting Measurements Augmented with Simulation
This paper describes the generation of 39Ar, via reactor irritation of potassium carbonate, followed by quantitative analysis (length-compensated proportional counting) to yield calibration standards that are approximately 50 and 3 times atmospheric background levels. Measurements were performed in Pacific Northwest National Laboratory’s shallow underground counting laboratory studying the effect of gas density on beta-transport; these results are compared with simulation. The total expanded uncertainty of the specific activity for the ~50x 39Ar in P10 standard is 3.6% (k=2).
Absolute radiation measurement↗
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
Not provided.
Automation & Control Systems↗
Machine Learning Augmented Predictive and Generative Model for Rupture Life in Ferritic and Austenitic Steels
The Larson-Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high temperature applications. However, owing to poor generalizability and dependence on the constant C, which is typically not known a-priori, estimations using the Larson-Miller parameter often result in suboptimal performance for a wide range of materials. At best it is useful in comparing alloys of similar composition. In this work, three machine learning (ML) schemes were developed for rupture life prediction for 9-12% Cr ferritic-martensitic steels and austenitic stainless steels, i.e., a hierarchical model to parameterize LMP using the LMP constant C to compute rupture life, a hierarchical model to parameterize both C and LMP to compute rupture life, and a direct prediction of rupture life. Specifically, we show that the third scheme, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pear-son Correlation Coefficient > 0.9 for 9-12% Cr and > 0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Furthermore, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9-12 % Cr ferritic-martensitic steel and austenitic stainless steel datasets. Finally, based on the predictive and generative model, a reinforcement learning strategy has been proposed to guide experimentalists into designing better heat resistant alloys.
Mamun, Md Osman G.↗
Augmenting collider searches and enhancing discovery potentials through stochastic jet grooming
Not Available
Roy, Tuhin S.↗
LARGE ACOUSTOELECTRIC EFFECT IN WAFER BONDED INDIUM GALLIUM ARSENIDE / LITHIUM NIOBATE HETEROSTRUCTURE AUGMENTED BY NOVEL GATE CONTROL.
Abstract not provided.
Siddiqui, Aleem↗
Phenomenological versus random data augmentation for hyperspectral target detection.
Abstract not provided.
Zollweg, Joshua David↗
Flexspander: augmenting expander networks in high-performance systems with optical bandwidth steering
Communication efficiency is one of the deciding factors in determining many of today’s high-performance computing (HPC) applications. Traditionally, HPC systems have been on static network topologies, making them inflexible to the variety of skewed traffic demands that may arise due to the spatial locality inherent in many applications. To handle traffic locality, researchers have proposed integrating optical circuit switches (OCSs) into the network architecture, which reconfigures the network topology to alter and dynamically adapt to the predicted traffic. In this paper, we present a novel reconfigurable network topology called Flexspander. Beyond offering a flexible interconnect, Flexspander also offers full flexibility in terms of construction and can be built with any arbitrary combination of commercial electrical packet switches and OCSs. We evaluate Flexspander performance through extensive simulations with multiple network traces, and our results show improved performance for Flexspander over currently proposed static and reconfigurable topologies in terms of the flow completion time.
Teh, Min Yee↗
Enhancing Electron Microscopy Image Classification Using Data Augmentation
Manual labeling for machine learning tasks such as image classification is tedious and labor-intensive; as a result, scientific datasets suitable for deep learning applications are scarce and limited. While data augmentation techniques have shown promise for extending image datasets, very little work has been done to understand the impact of combining multiple augmentation methods sequentially or the limits of their effectiveness when combined. Our work addresses this gap by examining how standard and combinatorial data augmentation affects the performance of machine learning models when trained on small datasets for label classification tasks. For our analysis, we generate single, double and quadruple-augmented datasets for a microscopy image classification task using six standard augmentation methods, and compare the resultant improvements observed in binary classification accuracy with three standard image classification models (DenseNet169, MobileNetV2, ResNet101V2). Our experiments show a non-monotonic relationship between the number of simultaneous augmentation methods and classification accuracy, indicating that there is a trade-off between the degree of augmentation and the model performance. These findings suggest that the optimal number of augmentation methods will vary by domain and use case. We also find that the order in which augmentation methods are applied to a limited dataset matters when combining augmentation schemes, with our use case showing performance differences up to 2.6% when the augmentation order is reversed for double-augmented datasets. Our work offers insights to the limits of data augmentation when working on image classification tasks with limited datasets.
Welsman, Jordan A↗
Augmented Reality Tools for Nuclear Facilities - 18318
In this work we explore the possibility of improving nuclear facility operations through the use of emerging augmented reality technology. Specifically we focus on the use of augmented reality headsets. Augmented reality opens up the possibility for easy, on-the-fly access to documentation related to canisters, rooms, and carts. It also can be used to help plan complicated material moves in ways that do not violate nuclear criticality safety regulations. Modern augmented reality technology can also be interacted with using gaze, gesture and voice commands, thus making it particularly attractive for facilitating the documentation of nuclear material operations and work verification. An augmented reality-based nuclear facility criticality safety system would have a number of benefits for operations in nuclear facilities. This work could be used across the DOE complex to ensure technicians in gloveboxes work safely and securely. It could greatly reduce downtime associated with administrative criticality incidents. It would be a step forward in the process of improving the quality of documentation of the work that occurs in nuclear facilities while simultaneously increasing the ease with which this documentation can be collected. The augmented reality effectively endows the glovebox technicians with a sense for administrative nuclear criticality safety and the reactivity changes caused directly by their actions. Augmented reality also has great potential for facilitating inspections of nuclear infrastructure. Examples of Augmented Reality applications that facilitate infrastructure inspection and work verification will also be presented. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Percussive Augmenter of Rotary Drills (PARoD)
A percussive augmenter is being developed and it is designed to add ultrasonic hammering to a conventional commercial rotary drill through an adapter using a piezoelectric actuator. The combination of ultrasonic hammering and rotation creates a drill that requires low power and low axial load while providing faster penetration than conventional rotary-only drills. These characteristics make percussive augmentation of rotary drills ideal for not only planetary exploration but commercial applications as well. The purpose of this internship was to produce, test, and optimize an augmenter that drives a 2 inch diameter bit. This task was part of the percussive augmenter's phase II of an ongoing contract and it involved increasing the capability of the .25 inch version of the augmenter prototype that was developed in phase I. Due to fabrication delays of the augmenter, an extensive part of the test effort was conducted using previous rotary and hammer drill prototypes. These tests focused on drilling deep over long periods of time to provide for effective stress test of the drill. Modifications were made to the drill, its components, and the testing procedure to achieve desired borehole depths. These results were used to start initial testing on the 2 inch augmenter and provide preliminary data on the augmenter's ability to significantly improve penetration rate over conventional rotary-only drills.
rotraty hammer↗
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies. A primary challenge in modeling drug response prediction (DRP) with PDXs and neural networks (NNs) is the limited number of drug response samples. We investigate multimodal neural network (MM-Net) and data augmentation for DRP in PDXs. The MM-Net learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs). We explore whether combining WSIs with GE improves predictions as compared with models that use GE alone. We propose two data augmentation methods which allow us training multimodal and unimodal NNs without changing architectures with a single larger dataset: 1) combine single-drug and drug-pair treatments by homogenizing drug representations, and 2) augment drug-pairs which doubles the sample size of all drug-pair samples. Unimodal NNs which use GE are compared to assess the contribution of data augmentation. The NN that uses the original and the augmented drug-pair treatments as well as single-drug treatments outperforms NNs that ignore either the augmented drug-pairs or the single-drug treatments. In assessing the multimodal learning based on the MCC metric, MM-Net outperforms all the baselines. Our results show that data augmentation and integration of histology images with GE can improve prediction performance of drug response in PDXs.
60 APPLIED LIFE SCIENCES↗
Thrust Augmentation of a Turbojet Engine at Simulated Flight Conditions by Introduction of a Water-Alcohol Mixture into the Compressor
An investigation was conducted at simulated high-altitude flight conditions to evaluate the use of compressor evaporative cooling as a means of turbojet-engine thrust augmentation. Comparison of the performance of the engine with water-alcohol injection at the compressor inlet, at the sixth stage of the compressor, and at the sixth and ninth stages was made. From consideration of the thrust increases achieved, the interstage injection of the coolant was considered more desirable preferred over the combined sixth- and ninth-stage injection because of its relative simplicity. A maximum augmented net-thrust ratio of 1.106 and a maximum augmented jet-thrust ratio of 1.062 were obtained at an augmented liquid ratio of 2.98 and an engine-inlet temperature of 80 F. At lower inlet temperatures (-40 to 40 F), the maximum augmented net-thrust ratios ranged from 1.040 to 1.076 and the maximum augmented jet-thrust ratios ranged from 1.027 to 1.048, depending upon the inlet temperature. The relatively small increase in performance at the lower inlet-air temperatures can be partially attributed to the inadequate evaporation of the water-alcohol mixture, but the more significant limitation was believed to be caused by the negative influence of the liquid coolant on engine- component performance. In general, it is concluded that the effectiveness of the injection of a coolant into the compressor as a means of thrust augmentation is considerably influenced by the design characteristics of the components of the engine being used.
Useller, James W.↗
Studies of Operating Frequency Effects On Ejector-based Thrust Augmentation in a Pulse Detonation Engine
Studies were performed in order to characterize the thrust augmentation potential of an ejector in a Pulse Detonation Engine application. A 49-mm diameter tube of 0.914-m length was constructed with one open end and one closed end. Ethylene, oxygen, and nitrogen were introduced into the tube at the closed end through the implementation of a fast mixing injector. The tube was completely filled with a stoichiometric mixture containing a one to one molar ratio of nitrogen to oxygen. Ethylene was selected as the fuel due to its detonation sensitivity and the molar ratio of the oxidizer was chosen for heat transfer purposes. Detonations were initiated in the tube through the use of a spark ignition system. The PDE was operated in a multi-cycle mode at frequencies ranging from 20-Hz to 50-Hz. Baseline thrust measurements with no ejector present were performed while operating the engine at various frequencies and compared to theoretical estimates. The baseline values were observed to agree with the theoretical model at low operating frequencies and proved to be increasingly lower than the predicted values as the operating frequency was increased. The baseline thrust measurements were observed to agree within 15 percent of the model for all operating frequencies. A straight 152-mm diameter ejector was installed and thrust augmentation percentages were measured. The length of the ejector was varied while the overlap percentage (percent of the ejector length which overlapped the tube) was maintained at 25 percent for all tests. In addition, the effect of ejector inlet geometry was investigated by comparing results with a straight inlet to those of a 38-mm inlet diameter. The thrust augmentation of the straight inlet ejector proved to be independent of engine operating frequency, augmenting thrust by 40 percent for the 0.914-m length ejector. In contrast, the rounded lip ejector of the same length seemed to be highly dependent on the engine operating frequency. An optimum operating frequency observed with the rounded inlet occurred at an operating frequency of 30-Hz, resulting in thrust augmentation percentages greater than 100 percent. The effect that the engine operating frequency had on thrust augmentation levels attained with an ejector was characterized and optimum performance parameters were established. Insight into the frequency dependent nature of the ejector performance was pursued. Suggestions for future experiments which are needed to fully understand the means in which thrust augmentation is achieved in a PDE-ejector configuration were noted.
Landry, K.↗
Augmented Reality for Managing Nuclear Infrastructure and Operations - 19605
Emerging augmented reality tools offer great promise for helping manage nuclear infrastructure throughout its life cycle. These tools have the ability to provide operators with actionable holographic information as they work. Perhaps more excitingly these tools also feature sensors that provide the ability to document work at very high spatial resolution. These sensors include onboard cameras as well as active infrared sensors that can capture 3D geometry information on-the-fly. Coupling the visualization and sensing tools provided by augmented reality headsets with computational intelligence opens up many exciting new possibilities for managing nuclear infrastructure throughout its life cycle. In this presentation we show a number of examples of how augmented reality tools can be incorporated into the management of nuclear infrastructure. We show examples of using augmented reality as a human-machine interface for robotic control, and we show examples of augmented reality being interfaced with external sensors. Finally, we show how these sensing and visualization capabilities of augmented reality can be interfaced with intelligent algorithms to help operators perform their work in nuclear facilities. The goal of the work is to spark discussions on how emerging augmented reality tools can help enable the next generation of efficient, high-performance nuclear infrastructure. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Making Invisible Visible: Data-Driven Seismic Inversion With Spatio-Temporally Constrained Data Augmentation
Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this article, we develop new data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO 2 leakage data. Our interest is to invert for subsurface velocity models associated with very small CO 2 leakage. We validate the performance of our methods using comprehensive numerical tests. Here via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques.
58 GEOSCIENCES↗
Orion MPCV Continuum RCS Heating Augmentation Model Development
The reaction control system jets of the Orion Multi Purpose Crew Vehicle can have a significant impact on the magnitude and distribution of the surface heat flux on the leeside of the aft-body, when they are fired. Changes in surface heating are expressed in terms of augmentation factor over the baseline smooth body heating. Wind tunnel tests revealed heating augmentation factors as high as 13.0, 7.6, 2.8, and 5.8 for the roll, pitch down, pitch up, and yaw jets respectively. Heating augmentation factor models, based almost exclusively on data from a series of wind tunnel tests have been developed, for the purposes of thermal protection system design. The wind tunnel tests investigated several potential jet-to-freestream similarity parameters, and heating augmentation factors derived from the data showed correlation with the jet-to-freestream momentum ratio. However, this correlation was not utilized in the developed models. Instead augmentation factors were held constant throughout the potential trajectory space. This simplification was driven by the fact that ground to flight traceability and sting effects are not well understood. Given the sensitivity of the reaction control system jet heating augmentation to configuration, geometry, and orientation the focus in the present paper is on the methodology used to develop the models and the lessons learned from the data. The models that are outlined in the present work are specific to the aerothermal database used to design the thermal protection system for the Exploration Flight Test 1 vehicle.
Heating Augmentation↗