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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Simple Space Vector Modulation of High-Frequency AC Linked Three-Phase-to-Single-Phase/DC Converter

High frequency (HF) ac linked converter functions well to interface between buses with different voltage types. Using a three-phase-to-single-phase matrix converter, a single-phase cyclo converter, and a HF transformer, we can compose a HF ac linked converter to interface between three-phase ac grids and single-phase ac or dc distributed generations (DGs). In this paper, a novel space vector modulation (SVM) method is proposed for such kind of converter. The proposed SVM method converts the mains three-phase ac voltage to a HF ac voltage at the primary side of transformer, and then unfolds the HF ac voltage to a single-phase ac or dc voltage at the secondary side of transformer. Without need to compute separate switching signals for the primary and secondary converters and then coordinate their operations elaborately, the proposed SVM method can be easily implemented by incorporating the HF chopping into the SVM of traditional indirect matrix converter. Therefore, no complicated duty cycle computation or elaborate switching states combination is required. Simulation and hardware-in-loop implementation demonstrate the validity of the proposed method.

42 ENGINEERING↗
DOE OSTI2019

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

Identifying the change point of a system’s health status is critical. Indeed, a change point usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection that could be used for identifying change points; yet, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. Our method uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data.

42 ENGINEERING↗
DOE OSTI2018

Fast QSTS for Distributed PV Impact Studies Using Vector Quantization and Variable Time-Steps

As the penetration level of PV systems increases, utilities are in great need of methods to quickly and effectively analyze PV's impact on electric distribution systems. Compared to other conventional methods, quasi-static time-series (QSTS) based analysis has demonstrated its advantages of accurately capturing the impact of interest, e.g. control device operations, and min and max voltages. However, yearlong QSTS simulation with 1-second granularity is currently too computationally expensive for regular use. Therefore, vector quantization (VQ) and variable time-step (VTS) solution methods have been developed to accelerate QSTS simulation. This paper proposes the combination of VQ with VTS. We investigate the effectiveness of implementing VQ in combination with 2 VTS algorithms, predetermined time-step (PT) and VTS with backtrack downsampling (VTS-BD) to demonstrate a runtime reduction of 99.55% with error metrics below acceptable thresholds.

14 SOLAR ENERGY↗
DOE OSTI2013

Exploring Vector Fields with Distribution-based Streamline Analysis

Streamline-based techniques are designed based on the idea that properties of streamlines are indicative of features in the underlying field. In this paper, we show that statistical distributions of measurements along the trajectory of a streamline can be used as a robust and effective descriptor to measure the similarity between streamlines. With the distribution-based approach, we present a framework for interactive exploration of 3D vector fields with streamline query and clustering. Streamline queries allow us to rapidly identify streamlines that share similar geometric features to the target streamline. Streamline clustering allows us to group together streamlines of similar shapes. Based on users selection, different clusters with different features at different levels of detail can be visualized to highlight features in 3D flow fields. We demonstrate the utility of our framework with simulation data sets of varying nature and size.

Streamline Analysis↗
DOE OSTI2019

Islanding Detection of Distributed Generation Using Electrical Variables in Space Vector Domain

This paper presents a new approach for islanding detection in distribution systems incorporating synchronous generator-based distributed generations. The proposed method first utilizes the dynamic equations of three electrical variables under islanding situations. The variables are: change of frequency; rate of change of frequency; and rotor angle deviation. Second, transformation of these variables is conducted in the space vector domain, which gives rise to three parameters, namely, semi-major axis, semi-minor axis, and inclination angle. Finally, exploiting these parameters, a decision boundary is established to classify grid events resulting in the formation of an island or not. The proposed method is comprehensively investigated under different operating conditions, which include excess/deficit of reactive/active power imbalance scenarios in the formed island containing different load-types (constant impedance, constant power, and constant current) with varying quality factor. A large variety of islanding and non-islanding events are generated using a practical distribution network simulated in real time on OPAL-RT digital simulation platform under the aforementioned operating conditions. A detailed evaluation of the proposed approach is conducted and it demonstrates the effectiveness of the method.

F-measure↗
DOE OSTI2016

SU-F-J-77: Variations in the Displacement Vector Fields Calculated by Different Deformable Image Registration Algorithms Used in Helical, Axial and Cone-Beam CT Images of a Mobile

Purpose: To investigate quantitatively the displacement-vector-fields (DVF) obtained from different deformable image registration algorithms (DIR) in helical (HCT), axial (ACT) and cone-beam CT (CBCT) to register CT images of a mobile phantom and its correlation with motion amplitudes and frequencies. Methods: HCT, ACT and CBCT are used to image a mobile phantom which includes three targets with different sizes that are manufactured from water-equivalent material and embedded in low density foam. The phantom is moved with controlled motion patterns where a range of motion amplitudes (0–40mm) and frequencies (0.125–0.5Hz) are used. The CT images obtained from scanning of the mobile phantom are registered with the stationary CT-images using four deformable image registration algorithms including demons, fast-demons, Horn-Schunk and Locas-Kanade from DIRART software. Results: The DVF calculated by the different algorithms correlate well with the motion amplitudes that are applied on the mobile phantom where maximal DVF increase linearly with the motion amplitudes of the mobile phantom in CBCT. Similarly in HCT, DVF increase linearly with motion amplitude, however, its correlation is weaker than CBCT. In ACT, the DVF’s do not correlate well with the motion amplitudes where motion induces strong image artifacts and DIR algorithms are not able to deform the ACT image of the mobile targets to the stationary targets. Three DIR-algorithms produce comparable values and patterns of the DVF for certain CT imaging modality. However, DVF from fast-demons deviated strongly from other algorithms at large motion amplitudes. Conclusion: In CBCT and HCT, the DVF correlate well with the motion amplitude of the mobile phantom. However, in ACT, DVF do not correlate with motion amplitudes. Correlations of DVF with motion amplitude as in CBCT and HCT imaging techniques can provide information about unknown motion parameters of the mobile organs in real patients as demonstrated in this phantom visibility study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Extremal vectors for Verma-type representations of su(2, 2)

Starting from the Verma modules of the algebra sl(4, Double-Struck-Capital-C ) we explicitly construct factor representations of the algebra su(2, 2) which are connected with unitary representation of group SU(2, 2). We find a full set of extremal vectors for this kind of representations, so we can solve explicitly the problem of irreducibility of these representations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Form factor for a two-particle system within a relativistic quasipotential approach: Case of arbitrary masses and of a vector current

A new relativistic form factor for a bound two-particle system was obtained for the case of a vector current. The present consideration was performed within the relativistic quasipotential approach based on the covariant Hamiltonian formulation of quantum field theory by going over to the three-dimensional relativistic configuration representation for the case of interaction between two relativistic spinless particles of arbitrary mass.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Form factor of the longitudinal component of weak vector current in pion beta decay

The form factor f{sub -} of the longitudinal component of the weak vector current in pion β-decay is derived using the generalized Ward identity. The form factor is expressed through the pion mass difference and mean-square radii in the channels with angular momenta J = 1, 0 and isospins T = 1, 2.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Microtomography of the Baltic amber tick Ixodes succineus reveals affinities with the modern Asian disease vector Ixodes ovatus

Background: Fossil ticks are extremely rare, whereby Ixodes succineus Weidner, 1964 from Eocene (ca. 44–49 Ma) Baltic amber is one of the oldest examples of a living hard tick genus (Ixodida: Ixodidae). Previous work suggested it was most closely related to the modern and widespread European sheep tick Ixodes ricinus (Linneaus, 1758). Results: Restudy using phase contrast synchrotron x-ray tomography yielded images of exceptional quality. These confirm the fossil’s referral to Ixodes Latreille, 1795, but the characters resolved here suggest instead affinities with the Asian subgenus Partipalpiger Hoogstraal et al., 1973 and its single living (and medically significant) species Ixodes ovatus Neumann, 1899. We redescribe the amber fossil here as Ixodes (Partipalpiger) succineus. Conclusions: Our data suggest that Ixodes ricinus is unlikely to be directly derived from Weidner’s amber species, but instead reveals that the Partipalpiger lineage was originally more widely distributed across the northern hemisphere. The closeness of Ixodes (P.) succineus to a living vector of a wide range of pathogens offers the potential to correlate its spatial and temporal position (northern Europe, nearly 50 million years ago) with the estimated origination dates of various tick-borne diseases.

Arachnida, Parasitiformes, Ixodida, parasite, vect↗
DOE OSTI2020

Benders Cut Classification via Support Vector Machines for Solving Two-Stage Stochastic Programs

In this work, we consider Benders decomposition for solving two-stage stochastic programs with complete recourse based on finite samples of the uncertain parameters. We define the Benders cuts binding at the final optimal solution or the ones significantly improving bounds over iterations as valuable cuts. We propose a learning-enhanced Benders decomposition (LearnBD) algorithm, which adds a cut classification step in each iteration to selectively generate cuts that are more likely to be valuable cuts. The LearnBD algorithm includes two phases: (i) sampling cuts and collecting information from training problems and (ii) solving testing problems with a support vector machine (SVM) cut classifier. We run the LearnBD algorithm on instances of capacitated facility location and multicommodity network design under uncertain demand. Our results show that SVM cut classifier works effectively for identifying valuable cuts, and the LearnBD algorithm reduces the total solving time of all instances for different problems with various sizes and complexities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Vector meson production in ultraperipheral heavy ion collisions

We review model calculations of exclusive vector meson production in ultraperipheral heavy ion collisions. We highlight differences and similarities between different dipole models and leading twist shadowing calculations. Recent color glass condensate calculations are presented with focus on effects from nuclear structure and azimuthal anisotropies driven by interference effects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Sensor network based solar forecasting using a local vector autoregressive ridge framework

The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Vector Boson Scattering Studies and Upgrades of Frontend Electronics for the ATLAS Muon Spectrometer

I report the research activities that my group has with the ATLAS experiment at the LHC for the period of 7/2017 to 3/2018. The research focused on observation of the same-sign WW vector boson scattering process, evidence of the WWW production, and search for new resonances in the WV final state. In addition, I also describe our work on the development of frontend electronics for Phase-1 and Phase-2 upgrades of the ATLAS muon spectrometer.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Higgs Portal Vector Dark Matter Interpretation: Review of Effective Field Theory Approach and Ultraviolet Complete Models

In this report a review of the Higgs portal-vector dark matter interpretation of the spin-independent dark matter nu-cleon elastic scattering cross section is presented, where the invisible Higgs decay width measured at the LHC is used. Effective Field Theory and ultraviolet complete models are discussed. LHC interpretations show only the scalar and Majorana dark matter scenarios; we propose including interpretation for vec-tor dark matter in the EFT and UV completion theoretical framework. In addition, our studies suggest an extension of the LHC dark matter interpretations to the sub-GeV regime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam

Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.

60 APPLIED LIFE SCIENCES↗