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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The analytic structure of non-global logarithms: Convergence of the dressed gluon expansion

Non-global logarithms (NGLs) are the leading manifestation of correlations between distinct phase space regions in QCD and gauge theories and have proven a challenge to understand using traditional resummation techniques. Recently, the dressed gluon ex-pansion was introduced that enables an expansion of the NGL series in terms of a “dressed gluon” building block, defined by an all-orders factorization theorem. Here, we clarify the nature of the dressed gluon expansion, and prove that it has an infinite radius of convergence as a solution to the leading logarithmic and large-N c master equation for NGLs, the Banfi-Marchesini-Smye (BMS) equation. The dressed gluon expansion therefore provides an expansion of the NGL series that can be truncated at any order, with reliable uncertainty estimates. In contrast, manifest in the results of the fixed-order expansion of the BMS equation up to 12-loops is a breakdown of convergence at a finite value of α s log. We explain this finite radius of convergence using the dressed gluon expansion, showing how the dynamics of the buffer region, a region of phase space near the boundary of the jet that was identified in early studies of NGLs, leads to large contributions to the fixed order expansion. We also use the dressed gluon expansion to discuss the convergence of the next-to-leading NGL series, and the role of collinear logarithms that appear at this order. Finally, we show how an understanding of the analytic behavior obtained from the dressed gluon expansion allows us to improve the fixed order NGL series using conformal transformations to extend the domain of analyticity. Furthermore, this allows us to calculate the NGL distribution for all values of α s log from the coefficients of the fixed order expansion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Inherent Driving Force for Charge Separation in Curved Stacks of Oligothiophenes

Coexistence of high local charge mobility and an energy gradient can lead to efficient free charge carrier generation from geminate charge transfer states at the donor–acceptor interface in bulk heterojunction organic photovoltaics. It is, however, not clear what polymer microstructures can support such coexistence. Using recent methods from density functional theory, we propose that a stack of similarly curved oligothiophene chains can deliver the requirements for efficient charge separation. Curved stacks are stable because of the polymer’s strong π-stacking ability and because backbone torsions are flexible in neutral chains. However, energy of a charge in a polymer chain has remarkably stronger dependence on torsions. The trend of increasing planarity in curved stacks effectively creates an energy gradient that drives charge in one direction. The curvature of these partially ordered stacks is found to beneficially interact with fullerenes for charge separation. The curved stacks, therefore, are identified as possible building blocks for interfacial structures that lead to efficient free carrier generation in high-performing organic photovoltaic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Catalytic conversion of high S-lignin to a sustainable tri-epoxide polymer precursor

More than 40 million tons of thermosetting plastics are produced annually and 70% of those are epoxy polymers. The synthesis of bio-based epoxide provides a pathway for making renewable thermoset plastics. We describe in this study the use of a genetically modified high-S poplar lignin to produce 4-propyl-2,6-dimethoxyphenol (DMPP), which is converted to the tri-functional compound propylpyrogallol (DMPPO) by a reaction catalysed by N 2 O 5 in the green solvent water. The resulting DMPPO can be converted to a novel tri-epoxide by reaction with epichlorohydrin. Native high-S lignin as well as several organosolv lignins extracted from this poplar biomass were investigated as feedstocks. Notably, direct reductive catalytic fractionation (RCF) of the high-S poplar wood over Pd–Zn/C catalyst with Pd : Zn ratio 1 : 10 at 225 °C under 35 bar H 2 in methanol gave the highest yield of biophenol monomers from the lignin present, the major product being DMPP. Under optimized conditions, conversion of DMPP to DMPPO over the Nb 2 O 5 was nearly quantitative (96% yield), and this conversion could be made without extensive prior purification of the DMPP. The Nb 2 O 5 catalyst could be recycled several times before significant deactivation. This sequence of two catalytic reactions demonstrates that the production of epoxides from lignin-derived DMPPO is a realistic strategy for making renewable polymer building blocks from biomass.

09 BIOMASS FUELS↗
DOE OSTI2025

HunStat2 – a simple and low-cost potentiostat with electrochemical impedance spectroscopy capability

We have developed a low-cost (30 USD), simple do-it-yourself (DIY) potentiostat with cyclic voltammetry (CV), open circuit potential (OCP) and electrochemical impedance spectroscopy (EIS) capability. The HunStat2 potentiostat is based on Analog Devices' AD5941 Analog Front End chip, which significantly simplifies the construction of potentiostats for both direct and alternating current (DC and AC, respectively) techniques. Interested readers are provided with circuit diagrams and a bill of materials to build the potentiostat on their own. In addition, control software is also provided free of charge. The software enables acquisition and visualization of data. In summary, HunStat2 introduces a simple and low-cost DIY potentiostat recommended for both analytical and educational purposes.

Vamos, Istvan [Lajos Petrik Vocational Chemistry S↗
DOE OSTI2015

Accurate high level ab initio-based global potential energy surface and dynamics calculations for ground state of CH{sub 2}{sup +}

A global many-body expansion potential energy surface is reported for the electronic ground state of CH{sub 2}{sup +} by fitting high level ab initio energies calculated at the multireference configuration interaction level with the aug-cc-pV6Z basis set. The topographical features of the new global potential energy surface are examined in detail and found to be in good agreement with those calculated directly from the raw ab initio energies, as well as previous calculations available in the literature. In turn, in order to validate the potential energy surface, a test theoretical study of the reaction CH{sup +}(X{sup 1}Σ{sup +})+H({sup 2}S)→C{sup +}({sup 2}P)+H{sub 2}(X{sup 1}Σ{sub g}{sup +}) has been carried out with the method of time dependent wavepacket on the title potential energy surface. The total integral cross sections and the rate coefficients have been calculated; the results determined that the new potential energy surface can both be recommended for dynamics studies of any type and as building blocks for constructing the potential energy surfaces of larger C{sup +}/H containing systems.

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

Molecular model of J-aggregated pseudoisocyanine fibers

Aggregated cyanines form ordered supramolecular structures with the potential to transport energy efficiently over long distances, a hallmark of photosynthetic light-harvesting complexes. In concentrated aqueous solution, pseudoisocyanine (PIC) spontaneously forms fibers with a chiral J-band red-shifted 1600 cm -1 from the monomeric 0–0 transition. A cryogenic transmission electron microscopy analysis of these fibers show an average fiber width of 2.89 nm, although the molecular-level structure of the aggregate is currently unknown. To determine a molecular model for these PIC fibers, the calculated spectra and dynamics using a Frenkel exciton model are compared to experiment. A chiral aggregate model in which the PIC monomers are neither parallel nor orthogonal to the long axis of the fiber is shown to replicate the experimental spectra most closely. This model can be physically realized by the sequential binding of PIC dimers and monomers to the ends of the fiber. Furthermore, these insights into the molecular aggregation model for aqueous PIC can also be applied to other similar cyanine-based supramolecular complexes with the potential for long-range energy transport, a key building block for the rational design of novel excitonic systems

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Thermal transport in defective and disordered materials

With significant recent advancements in thermal sciences—such as the development of new theoretical and experimental techniques, and the discovery of new transport mechanisms—it is helpful to revisit the fundamentals of vibrational heat conduction to formulate an updated and informed physical understanding. The increasing maturity of simulation and modeling methods sparks the desire to leverage these techniques to rapidly improve and develop technology through digital engineering and multi-scale, electro-thermal models. With that vision in mind, this review attempts to build a holistic understanding of thermal transport by focusing on the often unaddressed relationships between subfields, which can be critical for multi-scale modeling approaches. For example, we outline the relationship between mode-specific (computational) and spectral (analytical) models. We relate thermal boundary resistance models based on perturbation approaches and classic transmissivity based models. We discuss the relationship between lattice dynamics and molecular dynamics approaches along with two-channel transport frameworks that have emerged recently and that connect crystal-like and amorphous-like heat conduction. Throughout, we discuss best practices for modeling experimental data and outline how these models can guide material-level and system-level design.

42 ENGINEERING↗
DOE OSTI2013

A Novel Sensor Platform Matching the Improved Version of IPMVP Option C for Measuring Energy Savings

It is easy to measure energy consumption with a power meter. However, energy savings cannot be directly computed by the powers measured using existing power meter technologies, since the power consumption only reflects parts of the real energy flows. The International Performance Measurement and Verification Protocol (IPMVP) was proposed by the Efficiency Valuation Organization (EVO) to quantify energy savings using four different methodologies of A, B, C and D. Although energy savings can be estimated following the IPMVP, there are limitations on its practical implementation. Moreover, the data processing methods of the four IPMVP alternatives use multiple sensors (thermometer, hygrometer, Occupant information) and power meter readings to simulate all facilities, in order to determine an energy usage benchmark and the energy savings. This study proposes a simple sensor platform to measure energy savings. Using usually the Electronic Product Code (EPC) global standard, an architecture framework for an information system is constructed that integrates sensors data, power meter readings and occupancy conditions. The proposed sensor platform is used to monitor a building with a newly built vertical garden system (VGS). A VGS shields solar radiation and saves on energy that would be expended on air-conditioning. With this platform, the amount of energy saved in the whole facility is measured and reported in real-time. The data are compared with those obtained from detailed measurement and verification (M&V) processes. The discrepancy is less than 1.565%. Using measurements from the proposed sensor platform, the energy savings for the entire facility are quantified, with a resolution of ±1.2%. The VGS gives an 8.483% daily electricity saving for the building. Thus, the results show that the simple sensor platform proposed by this study is more widely applicable than the four complicated IPMVP alternatives and the VGS is an effective tool in reducing the carbon footprint of a building.

vertical garden system (VGS)↗
NASA NTRSDate not supplied

Guidance Enhancements and Performance Assessments for the Mars Ascent Vehicle Spin-Stabilized Upper Stage Configuration

he objective of the Mars Sample Return (MSR) campaign is to return samples from the surface of Mars to Earth for research. As one element of the MSR campaign, the Mars Ascent Vehicle (MAV) is responsible for transporting the samples from the surface of Mars to a Low-Martian Orbit (LMO) for retrieval. Complete autonomy is required throughout ascent, and orbital insertion is constrained by tight dispersion boundaries. An unguided, spin-stabilized second stage for MAV has been selected over a guided upper-stage to drive mass savings and reduce overall MSR campaign mass risk, at the cost of reduced GNC capability. To address this design change, the MAV GNC team has derived a robust prediction algorithm, building on previous energy management schemes, that solves for a single inertial pointing direction solution for the spin-stabilized 2nd stage burn. Algorithm stability is explored that compared to previous versions of the algorithm. Also, a set of analytical partials was developed to study MAV’s dispersed orbital insertion performance with respect to MAV system uncertainties. These partials were verified through simulation analysis and prove useful for analytical insight into the dynamics of MAV during the 2nd stage maneuver.

Jason M Everett↗
NASA NTRSDate not supplied

Guidance Enhancements and Performance Assessments for the Mars Ascent Vehicle Spin-Stabilized Upper Stage Configuration

The objective of the Mars Sample Return (MSR) campaign is to return samples from the surface of Mars to Earth for research. As one element of the MSR campaign, the Mars Ascent Vehicle (MAV) is responsible for transporting the samples from the surface of Mars to a Low-Martian Orbit (LMO) for retrieval. Complete autonomy is required throughout ascent, and orbital insertion is constrained by tight dispersion boundaries. An unguided, spin-stabilized second stage for MAV has been selected over a guided upper-stage to drive mass savings and reduce overall MSR campaign mass risk, at the cost of reduced GNC capability. To address this design change, the MAV GNC team has derived a robust prediction algorithm, building on previous energy management schemes, that solves for a single inertial pointing direction solution for the spin-stabilized 2nd stage burn. Algorithm stability is explored that compared to previous versions of the algorithm. Also, a set of analytical partials was developed to study MAV’s dispersed orbital insertion performance with respect to MAV system uncertainties. These partials were verified through simulation analysis and prove useful for analytical insight into the dynamics of MAV during the 2nd stage maneuver.

GNC↗
NASA NTRS2010

Mission Reliability Estimation for Repairable Robot Teams

A mission reliability estimation method has been designed to translate mission requirements into choices of robot modules in order to configure a multi-robot team to have high reliability at minimal cost. In order to build cost-effective robot teams for long-term missions, one must be able to compare alternative design paradigms in a principled way by comparing the reliability of different robot models and robot team configurations. Core modules have been created including: a probabilistic module with reliability-cost characteristics, a method for combining the characteristics of multiple modules to determine an overall reliability-cost characteristic, and a method for the generation of legitimate module combinations based on mission specifications and the selection of the best of the resulting combinations from a cost-reliability standpoint. The developed methodology can be used to predict the probability of a mission being completed, given information about the components used to build the robots, as well as information about the mission tasks. In the research for this innovation, sample robot missions were examined and compared to the performance of robot teams with different numbers of robots and different numbers of spare components. Data that a mission designer would need was factored in, such as whether it would be better to have a spare robot versus an equivalent number of spare parts, or if mission cost can be reduced while maintaining reliability using spares. This analytical model was applied to an example robot mission, examining the cost-reliability tradeoffs among different team configurations. Particularly scrutinized were teams using either redundancy (spare robots) or repairability (spare components). Using conservative estimates of the cost-reliability relationship, results show that it is possible to significantly reduce the cost of a robotic mission by using cheaper, lower-reliability components and providing spares. This suggests that the current design paradigm of building a minimal number of highly robust robots may not be the best way to design robots for extended missions.

Trebi-Ollennu, Ashitey↗
DOE OSTI2012

PHOTOMETRIC REDSHIFTS AND QUASAR PROBABILITIES FROM A SINGLE, DATA-DRIVEN GENERATIVE MODEL

We describe a technique for simultaneously classifying and estimating the redshift of quasars. It can separate quasars from stars in arbitrary redshift ranges, estimate full posterior distribution functions for the redshift, and naturally incorporate flux uncertainties, missing data, and multi-wavelength photometry. We build models of quasars in flux-redshift space by applying the extreme deconvolution technique to estimate the underlying density. By integrating this density over redshift, one can obtain quasar flux densities in different redshift ranges. This approach allows for efficient, consistent, and fast classification and photometric redshift estimation. This is achieved by combining the speed obtained by choosing simple analytical forms as the basis of our density model with the flexibility of non-parametric models through the use of many simple components with many parameters. We show that this technique is competitive with the best photometric quasar classification techniques-which are limited to fixed, broad redshift ranges and high signal-to-noise ratio data-and with the best photometric redshift techniques when applied to broadband optical data. We demonstrate that the inclusion of UV and NIR data significantly improves photometric quasar-star separation and essentially resolves all of the redshift degeneracies for quasars inherent to the ugriz filter system, even when included data have a low signal-to-noise ratio. For quasars spectroscopically confirmed by the SDSS 84% and 97% of the objects with Galaxy Evolution Explorer UV and UKIDSS NIR data have photometric redshifts within 0.1 and 0.3, respectively, of the spectroscopic redshift; this amounts to about a factor of three improvement over ugriz-only photometric redshifts. Our code to calculate quasar probabilities and redshift probability distributions is publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Learning to predict the cosmological structure formation

Matter evolved under the influence of gravity from minuscule density fluctuations. Nonperturbative structure formed hierarchically over all scales and developed non-Gaussian features in the Universe, known as the cosmic web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and use a large ensemble of computer simulations to compare with the observed data to extract the full information of our own Universe. However, to evolve billions of particles over billions of years, even with the simplest physics, is a daunting task. We build a deep neural network, the Deep Density Displacement Model ( D 3 M ), which learns from a set of prerun numerical simulations, to predict the nonlinear large-scale structure of the Universe with the Zel’dovich Approximation (ZA), an analytical approximation based on perturbation theory, as the input. Our extensive analysis demonstrates that D 3 M outperforms the second-order perturbation theory (2LPT), the commonly used fast-approximate simulation method, in predicting cosmic structure in the nonlinear regime. We also show that D 3 M is able to accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. Our study proves that deep learning is a practical and accurate alternative to approximate 3D simulations of the gravitational structure formation of the Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Spectral Analytics of Solar Photovoltaic Power Output for Optimal Distributed Energy Resource Utilization

This paper examines the spectral analytics of solar photovoltaic (PV) power in order to understand its frequency content. This information is then used to illustrate that the different frequency components of PV generation can be consumed locally by controlling local distributed energy resources in residential/commercial buildings. The solar PV generation signal is divided into three components: high frequency (second-level), medium frequency (minute-level), and low-frequency that correlate with the solar activity. One year of solar PV power data is analyzed with 1-second resolution to find the ideal bounds for the different frequency bands. Results show that by employing intelligent control of Heating, Ventilation and Air-Conditioning (HVAC) systems, HVAC loads are able to accommodate the low and medium frequency components of the PV generation while local energy storage systems can be used to offset the high frequency components. This demonstrates the ability to spatially-local consumption of PV generation using controllable loads so as to minimize impact on the grid, reduce size of storage devices, and increase solar PV penetration levels.

Olama, Mohammed M.↗
DOE OSTI2014

Charging and Heating Dynamics of Nanoparticles in Nonthermal Plasmas

The focus of this award was to understand the interactions of nanometer-sized particles with ionized gases, also called plasmas. Plasmas are widely used in the fabrication of electronic circuits such as microprocessors and memory devices, in plasma display panels, as well as in medical applications. Recently, these ionized gases are finding applications in the synthesis of advanced nanomaterials with novel properties, which are based on nanometer-sized particulate (nanoparticles) building blocks. As these nanoparticles grow in the plasma environment, they interact with the plasmas species such as electrons and ions which critically determines the nanoparticle properties. The University of Minnesota researchers conducting this project performed numerical simulations and developed analytical models that described the interaction of plasma-bound nanoparticles with the plasma ions. The plasma ions bombard the nanoparticle surface with substantial energy, which can result in the rearrangement of the nanoparticles’ atoms, giving them often desirable structures at the atomic scale. Being able to tune the ion energies allows to control the properties of nanoparticles produced in order to tailor their attributes for certain applications. For instance, when used in high efficiency light emitting devices, nanoparticles produced under high fluxes of highly energetic ions may show superior light emission to particles produced under low fluxes of less energetic ions. The analytical models developed by the University of Minnesota researchers enable the research community to easily determine the energy of ions bombarding the nanoparticles. The researchers extensively tested the validity of the analytical models by comparing them to sophisticated computer simulations based on stochastic particle modeling, also called Monte Carlo modeling, which simulated the motion of hundreds of thousands of ions and their interaction with the nanoparticle surfaces. Beyond the scientific intellectual merits, this award had significant broader impacts. Two graduate students received their doctoral degrees and both have joined a U.S. manufacturer of plasma-based semiconductor processing equipment. Four undergraduate students participated in research conducted under this grant and gained valuable hands-on laboratory experience. A middle school science teacher observed research conducted under this grant and developed three new course modules that introduce middle school students to the concepts of nanometer scale, the atomic structure of matter, and the composition of matter of different chemical elements.

42 ENGINEERING↗
NASA NTRS1999

Virtual Prototyping for Carbon Nanotubes and Their Compositions

Carbon nanotubes (CNT) are of interest as constituents-reinforcements for composites, probes for scanning microscopy, quantum wires for electronics, and building blocks for nanomachinery. Their small size makes direct experimental measurements difficult. Therefore, the critical answers about their properties should be obtained based on adequate modeling, virtual prototyping, using the methods of classical molecular dynamic (MD), in combination with the analytical methods and continuum mechanics.

Yakobson, Boris I.↗
NASA NTRS2006

Cascaded Perforates as One-Dimensional, Bulk Absorbers

Porous cell honeycomb liners for aircraft engine nacelles offer the possibility of exploiting extended reaction effects to improve liner attenuation bandwidth as generally attributed to the performance of bulk absorbers. This paper describes an analytical procedure, starting with an impedance prediction model for a single perforated plate, to estimate the bulk-absorber parameters for a cascade of such perforates - a first step to modeling a porous wall honeycomb structure. The objective is to build confidence in a lumped element impedance model, when applied to a uniformly-spaced set of porous plates to predict its .bulk. absorber properties. The model is based upon a modified version of the two-parameter flow resistance model of the form A + BV(sub inc), where A and B are physics-based, semi-empirical parameters that are adjusted to provide an optimum fit to a composite dataset from three plate porosities of 2.5, 5 and 10%. The composite dataset is achieved by reformulating the two-parameter flow resistance model into a .reduced pressure drop coefficient. dependency on perforate hole Reynolds number. The resulting impedance model is employed to calculate surface impedance spectra for N and 2N-layer perforate cascades. The well-known two-thickness method for experimental determination of bulk-absorber parameters is then applied to these .synthesized. data sets to predict the characteristic impedance and propagation constant for the perforate cascades. These results are then compared with experimental results reported in a companion paper.

Parrott, T. L.↗