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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Determining Series Resistance for Equivalent Circuit Models of a PV Module

Literature describes various methods for determining a series resistance for a photovoltaic device from measured IV curves. We investigate use of these techniques to estimate the series resistance parameter for a single diode equivalent circuit model. With simulated IV curves we demonstrate that the series resistance values obtained by these techniques differ systematically from the known series resistance parameter values used to generate the curves, indicating that these methods are not suitable for determining the series resistance parameter for the single diode model equation. Furthermore, we present an alternative method to determine the series resistance parameter jointly with the other parameters for the single diode model equation, and demonstrate the accuracy and reliability of this technique in the presence of measurement errors.

42 ENGINEERING↗
DOE OSTI2013

Substituent effects in a series of 1,7-C[subscript 60](R[subscript F])[subscript 2] compounds (R[subscript F] = CF[subscript 3], C[subscript 2]F[subscript 5], n-C[subscrip 3]F[subscript 7], i-C[subscript 3]F[subscript 7], n-C[subscript 4]F[subscript 9], s-C[subscript 4]F[subscript 9], n-C[subscript 8]F[subscript 17]): electron affinities, reduction potentials and E(LUMO) values are not always correlated

A series of seven structurally-similar compounds with different pairs of R{sub F} groups were prepared, characterized spectroscopically, and studied by electrochemical methods (cyclic and square-wave voltammetry), low-temperature anion photoelectron spectroscopy, and DFT calculations (five of the compounds are reported here for the first time). This is the first time that a set of seven R{sub F} groups have been compared with respect to their relative effects on E{sub 1/2}(0/-), electron affinity (EA), and the DFT-calculated LUMO energy. The compounds, 1,7-C{sub 60}(R{sub F}){sub 2} (R{sub F} = CF{sub 3}, C{sub 2}F{sub 5}, i-C{sub 3}F{sub 7}, n-C{sub 3}F{sub 7}, s-C{sub 4}F{sub 9}, n-C{sub 4}F{sub 9} and n-C{sub 8}F{sub 21}), were found to have statistically different electron affinities (EA), at the {+-}10 meV level of uncertainty, but virtually identical first reduction potentials, at the {+-}10 mV level of uncertainty. The lack of a correlation between EA and E{sub 1/2}(0/-), and between E(LUMO) and E{sub 1/2}(0/-), for such similar compounds is unprecedented and suggests that explanations for differences in figures of merit for materials and/or devices that are based on equating easily measurable E{sub 1/2}(0/-) values with EAs or E(LUMO) values should be viewed with caution. The solubilities of the seven compounds in toluene varied by nearly a factor of six, but in an unpredictable way, with the C{sub 2}F{sub 5} and s-C{sub 4}F{sub 9} compounds being the most soluble and the i-C{sub 3}F{sub 7} compound being the least soluble. The effects of the different R{sub F} groups on EAs, E(LUMO) values, and solubilities should help fluorine chemists choose the right R{sub F} group to design new materials with improved morphological, electronic, optical, and/or magnetic properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Variable Time-Step Implementation for Rapid Quasi-Static Time-Series (QSTS) Simulations of Distributed PV

Distribution system analysis with high penetrations of distributed PV require quasi-static time-series (QSTS) analysis to model the variability introduced on the distribution system, but current QSTS algorithms are prohibitively burdensome and computationally intensive. This paper proposes a variable timestep algorithm to calculate the critical time periods when QSTS simulations should be solved at higher or lower time-resolution and to backtrack for any critical periods that were missed. This variable time-step solver is a new method of performing timeseries simulations with high accuracy while performing the simulation more than 50 times faster. The scalability of the algorithm is demonstrated using a real utility distribution system model with thousands of buses.

14 SOLAR ENERGY↗
DOE OSTI2014

The application of complex network time series analysis in turbulent heated jets

In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topological properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Time Series Discord Detection in Medical Data using a Parallel Relational Database [PowerPoint]

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithms on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Extracting Uranium from Seawater: Promising AF Series Adsorbents

A new family of high-surface-area polyethylene fiber adsorbents named the AF series was recently developed at the Oak Ridge National Laboratory (ORNL). The AF series adsorbents were synthesized by radiation-induced graft polymerization of acrylonitrile and itaconic acid (at different monomer/comonomer mol ratios) onto high surface area polyethylene fibers. The degree of grafting (%DOG) of AF series adsorbents was found to be 154-354%. The grafted nitrile groups were converted to amidoxime groups by treating with hydroxylamine. The amidoximated adsorbents were then conditioned with 0.44 M KOH at 80 °C followed by screening at ORNL with sodium-based synthetic aqueous solution, spiked with 8 ppm uranium. The uranium adsorption capacity in simulated seawater screening ranged from 170 to 200 g-U/kg-ads irrespective of %DOG. A monomer/comonomer molar ratio in the range of 7.57-10.14 seemed to be optimum for highest uranium loading capacity. Subsequently, the adsorbents were also tested with natural seawater at Pacific Northwest National Laboratory (PNNL) using flow-through column experiments to determine uranium loading capacity with varying KOH conditioning times at 80 °C. The highest adsorption capacity of AF1 measured after 56 days of marine testing was demonstrated as 3.9 g-U/kg-adsorbent and 3.2 g-U/kg-adsorbent for 1 and 3 h of KOH conditioning at 80 °C, respectively. Based on capacity values of several AF1 samples, it was observed that changing KOH conditioning from 1 to 3 h at 80 °C resulted in a 22-27% decrease in uranium adsorption capacity in seawater.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1992

Ocean time-series near Bermuda: Hydrostation S and the US JGOFS Bermuda Atlantic time-series study

Bermuda is the site of two ocean time-series programs. At Hydrostation S, the ongoing biweekly profiles of temperature, salinity and oxygen now span 37 years. This is one of the longest open-ocean time-series data sets and provides a view of decadal scale variability in ocean processes. In 1988, the U.S. JGOFS Bermuda Atlantic Time-series Study began a wide range of measurements at a frequency of 14-18 cruises each year to understand temporal variability in ocean biogeochemistry. On each cruise, the data range from chemical analyses of discrete water samples to data from electronic packages of hydrographic and optics sensors. In addition, a range of biological and geochemical rate measurements are conducted that integrate over time-periods of minutes to days. This sampling strategy yields a reasonable resolution of the major seasonal patterns and of decadal scale variability. The Sargasso Sea also has a variety of episodic production events on scales of days to weeks and these are only poorly resolved. In addition, there is a substantial amount of mesoscale variability in this region and some of the perceived temporal patterns are caused by the intersection of the biweekly sampling with the natural spatial variability. In the Bermuda time-series programs, we have added a series of additional cruises to begin to assess these other sources of variation and their impacts on the interpretation of the main time-series record. However, the adequate resolution of higher frequency temporal patterns will probably require the introduction of new sampling strategies and some emerging technologies such as biogeochemical moorings and autonomous underwater vehicles.

Michaels, Anthony F.↗
DOE OSTI2020

Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation: Preprint

Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.

Bayesian structural time series↗
DOE OSTI2014

Chemical Degradation Studies on a Series of Dithiophosphinic Acids

A significant increase in the stability of a series of dithiophosphinic acids (DPAHs) under oxidizing acidic conditions was achieved. The degradation behavior of a series of DPAHs, designed for lanthanide/actinide separation, was examined. The stability of the DPAHs, when contacted with varying nitric acid concentrations, was tested and monitored using 31P {1H} NMR. Changes in the functional groups of the DPAHs resulted in substantial increases in the stability. However, when placed in contact with 2 M HNO3 all the DPAHs eventually showed signs of degradation. The addition of a radical scavenger, hydrazine, inhibited the degradation of the DPAHs. In the presence of a small concentration of hydrazine, five of the DPAHs remained stable for over a month in direct contact with 2 M HNO3.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Physics-Guided Deep Learning for Time-Series State Estimation Against False Data Injection Attacks

The modern power grid is a cyber-physical system. While the grid is becoming more intelligent with emerging sensing and communication techniques, new vulnerabilities are introduced and cyber security becomes a major concern. One type of cyber-attacks - False Data Injection Attacks (FDIAs) - exploits the limitations in traditional power system state estimation, and modifies system states without being detected. In this paper, we propose a physics-guided deep learning (PGDL) approach to defend against FDIAs. The PGDL takes real-time measurements as inputs to neural networks, outputs the estimated states, and reconstructs measurements considering power system physics. A deep recurrent neural network - Long Short-Term Memory (LSTM) - is employed to learn the temporal correlations among states. This hybrid learning model leads to a time-series state estimation method to defend against FDIAs. The simulation results using IEEE 14-bus test system demonstrate the accuracy and robustness of the proposed time-series state estimation under FDIAs.

Cyber security, state estimation, physics-guided d↗
DOE OSTI2020

Determination of Series Resistance in CdSeTe/CdTe Solar Cells by the Jsc–Voc Method

While commonly used in Si solar cell characterization, the Suns–Voc method of determining series resistance (Rs) is rarely used for CdTe and its alloys. However, it is advantageous relative to the slope method—commonly used in the CdTe community—because it measures series resistance at the maximum power point, at which a solar cell operates. Using the self-consistent predecessor to Suns–Voc, Jsc –Voc, , we determine Rs of two different CdSeTe/CdTe solar cell structures to be 0.96 and 2.72 Ωcm2, with repeated measurements showing little deviation

13 HYDRO ENERGY↗
DOE OSTI2014

On equivalence of high temperature series expansion and coupling parameter series expansion in thermodynamic perturbation theory of fluids

The coupling parameter series expansion and the high temperature series expansion in the thermodynamic perturbation theory of fluids are shown to be equivalent if the interaction potential is pairwise additive. As a consequence, for the class of fluids with the potential having a hardcore repulsion, if the hard-sphere fluid is chosen as reference system, the terms of coupling parameter series expansion for radial distribution function, direct correlation function, and Helmholtz free energy follow a scaling law with temperature. The scaling law is confirmed by application to square-well fluids.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗