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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 613 records · Page 34

Rapid Flux Phenotyping to Accelerate Metabolic Engineering of Cyanobacteria

This project established a rapid flux phenotyping platform to accelerate metabolic engineering of cyanobacterial hosts. As proof of principle, we applied this platform to optimize cyanobacterial production of isobutyraldehyde and ethylene in Synechococcus PCC 7942, as well as lysine and fatty acids in the fast-growing strain S ynechococcus PCC 7002. By matching the throughput of strain characterization to the rate of strain generation, this research provided the foundation for a rational “design-build-test-learn” cycle for metabolic engineering of cyanobacteria and other bioenergy-relevant microbes.

60 APPLIED LIFE SCIENCES↗

Probing Surface Hydration and Isotope Exchange Kinetics in Proton-Conducting Ceramics Using DRIFTS and EIS

This internship project investigates how water vapor partial pressure (pH2O) affects surface hydration and H/D isotope-exchange kinetics in BaZr0.4Ce0.4Y0.1Yb0.1O3?d (4411) proton-conducting ceramic electrolytes. Diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) is used to monitor surface hydroxyl and deuteroxyl species under controlled humidified atmospheres during gas-switching experiments. These surface kinetic trends are to be correlated with bulk, grain-boundary, and total electrolyte resistance measured by electrochemical impedance spectroscopy (EIS) under matching conditions. The future goals of this study are to clarify the relationship between surface hydration dynamics and proton conduction behavior, improving interpretation of EIS data and guiding the optimization of ceramic electrolytes for energy applications.

08 - HYDROGEN↗

AVHRR image navigation - Summary and review

The navigation of imagery from polar orbiting weather satellites includes the correction for geometric distortions due to earth shape/earth rotation, satellite orbit variations, and satellite attitude along with the resampling of the satellite image to a selected geographic map projection. The routine image navigation procedure also compensates for the distortion of the satellite imagery due to the nonlinear scanning of the sensor system. This paper reviews general methods for performing this image navigation, ranging from a method that assumes no orbital information and, thus, relies on nominal orbital parameter values and image corrections computed by matching ground control points (GCPs), to a method that uses high-quality satellite ephemeris data to make the correction with a limited number of GCPs. A procedure to optimize the image navigation by using a spatial remapping, or interpolation, is introduced and outlined. Recommendations are made for people interested in the processing of AVHRR imagery.

Emery, William J.↗

Astronomical data analysis software and systems I; Proceedings of the 1st Annual Conference, Tucson, AZ, Nov. 6-8, 1991

Consideration is given to a definition of a distribution format for X-ray data, the Einstein on-line system, the NASA/IPAC extragalactic database, COBE astronomical databases, Cosmic Background Explorer astronomical databases, the ADAM software environment, the Groningen Image Processing System, search for a common data model for astronomical data analysis systems, deconvolution for real and synthetic apertures, pitfalls in image reconstruction, a direct method for spectral and image restoration, and a discription of a Poisson imagery super resolution algorithm. Also discussed are multivariate statistics on HI and IRAS images, a faint object classification using neural networks, a matched filter for improving SNR of radio maps, automated aperture photometry of CCD images, interactive graphics interpreter, the ROSAT extreme ultra-violet sky survey, a quantitative study of optimal extraction, an automated analysis of spectra, applications of synthetic photometry, an algorithm for extra-solar planet system detection and data reduction facilities for the William Herschel telescope.

Worrall, Diana M.↗

HPCC Methodologies for Structural Design and Analysis on Parallel and Distributed Computing Platforms

In this grant, we have proposed a three-year research effort focused on developing High Performance Computation and Communication (HPCC) methodologies for structural analysis on parallel processors and clusters of workstations, with emphasis on reducing the structural design cycle time. Besides consolidating and further improving the FETI solver technology to address plate and shell structures, we have proposed to tackle the following design related issues: (a) parallel coupling and assembly of independently designed and analyzed three-dimensional substructures with non-matching interfaces, (b) fast and smart parallel re-analysis of a given structure after it has undergone design modifications, (c) parallel evaluation of sensitivity operators (derivatives) for design optimization, and (d) fast parallel analysis of mildly nonlinear structures. While our proposal was accepted, support was provided only for one year.

Farhat, Charbel↗

Generation of Parametric Equivalent-Area Targets for Design of Low-Boom Supersonic Concepts

A tool with an Excel visual interface is developed to generate equivalent-area (A(sub e)) targets that satisfy the volume constraints for a low-boom supersonic configuration. The new parametric Ae target explorer allows users to interactively study the tradeoffs between the aircraft volume constraints and the low-boom characteristics (e.g., loudness) of the ground signature. Moreover, numerical optimization can be used to generate the optimal A(sub e) target for given A(sub e) volume constraints. A case study is used to demonstrate how a generated low-boom Ae target can be matched by a supersonic configuration that includes a fuselage, wing, nacelle, pylon, aft pod, horizontal tail, and vertical tail. The low-boom configuration is verified by sonic-boom analysis with an off-body pressure distribution at three body lengths below the configuration

Li, Wu↗

ULTRA: Underwater Localization for Transit and Reconnaissance Autonomy

This software addresses the issue of underwater localization of unmanned vehicles and the inherent drift in their onboard sensors. The software gives a 2 to 3 factor of improvement over the state-of-the-art underwater localization algorithms. The software determines the localization (position, heading) of an AUV (autonomous underwater vehicle) in environments where there is no GPS signal. It accomplishes this using only the commanded position, onboard gyros/accelerometers, and the bathymetry of the bottom provided by an onboard sonar system. The software does not rely on an onboard bathymetry dataset, but instead incrementally determines the position of the AUV while mapping the bottom. In order to enable long-distance underwater navigation by AUVs, a localization method called ULTRA uses registration of the bathymetry data products produced by the onboard forward-looking sonar system for hazard avoidance during a transit to derive the motion and pose of the AUV in order to correct the DR (dead reckoning) estimates. The registration algorithm uses iterative point matching (IPM) combined with surface interpolation of the Iterative Closest Point (ICP) algorithm. This method was used previously at JPL for onboard unmanned ground vehicle localization, and has been optimized for efficient computational and memory use.

Huntsberger, Terrance L.↗

A Mars Sample Return Sample Handling System

We present a sample handling system, a subsystem of the proposed Dragon landed Mars Sample Return (MSR) mission [1], that can return to Earth orbit a significant mass of frozen Mars samples potentially consisting of: rock cores, subsurface drilled rock and ice cuttings, pebble sized rocks, and soil scoops. The sample collection, storage, retrieval and packaging assumptions and concepts in this study are applicable for the NASA's MPPG MSR mission architecture options [2]. Our study assumes a predecessor rover mission collects samples for return to Earth to address questions on: past life, climate change, water history, age dating, understanding Mars interior evolution [3], and, human safety and in-situ resource utilization. Hence the rover will have "integrated priorities for rock sampling" [3] that cover collection of subaqueous or hydrothermal sediments, low-temperature fluidaltered rocks, unaltered igneous rocks, regolith and atmosphere samples. Samples could include: drilled rock cores, alluvial and fluvial deposits, subsurface ice and soils, clays, sulfates, salts including perchlorates, aeolian deposits, and concretions. Thus samples will have a broad range of bulk densities, and require for Earth based analysis where practical: in-situ characterization, management of degradation such as perchlorate deliquescence and volatile release, and contamination management. We propose to adopt a sample container with a set of cups each with a sample from a specific location. We considered two sample cups sizes: (1) a small cup sized for samples matching those submitted to in-situ characterization instruments, and, (2) a larger cup for 100 mm rock cores [4] and pebble sized rocks, thus providing diverse samples and optimizing the MSR sample mass payload fraction for a given payload volume. We minimize sample degradation by keeping them frozen in the MSR payload sample canister using Peltier chip cooling. The cups are sealed by interference fitted heat activated memory alloy caps [5] if the heating does not affect the sample, or by crimping caps similar to bottle capping. We prefer cap sealing surfaces be external to the cup rim to prevent sample dust inside the cups interfering with sealing, or, contamination of the sample by Teflon seal elements (if adopted). Finally the sample collection rover, or a Fetch rover, selects cups with best choice samples and loads them into a sample tray, before delivering it to the Earth Return Vehicle (ERV) in the MSR Dragon capsule as described in [1] (Fig 1). This ensures best use of the MSR payload mass allowance. A 3 meter long jointed robot arm is extended from the Dragon capsule's crew hatch, retrieves the sample tray and inserts it into the sample canister payload located on the ERV stage. The robot arm has capacity to obtain grab samples in the event of a rover failure. The sample canister has a robot arm capture casting to enable capture by crewed or robot spacecraft when it returns to Earth orbit

Wilson, David↗

Random Variables with Moment-Matching Staircase Density Functions

This paper proposes a family of random variables for uncertainty modeling. The variables of interest have a bounded support set, and prescribed values for the first four moments. We present the feasibility conditions for the existence of any of such variables, and propose a class of variables that conforms to such constraints. This class is called staircase because the density of its members is a piecewise constant function. Convex optimization is used to calculate their distributions according to several optimality criteria, including maximal entropy and maximal log-likelihood. The flexibility and efficiency of staircases enable modeling phenomena having a possibly skewed and/or multimodal response at a low computational cost. Furthermore, we provide a means to account for the uncertainty in the distribution caused by estimating staircases from data. These ideas are illustrated by generating empirical staircase predictor models. We consider the case in which the predictor matches the sample moments exactly (a setting applicable to large datasets), as well as the case in which the predictor accounts for the sampling error in such moments (a setting applicable to sparse datasets). A predictor model for the dynamics of an aeroelastic airfoil subject to flutter instability is used as an example. The resulting predictor not only describes the system's response accurately, but also enables carrying out a risk analysis for safe flight.

Luis G. Crespo↗

Assessing uncertainty in the timing of energy use during cost-optimal distributed energy technology selection and sizing

This paper empirically derives uncertainty ranges in cost-optimal solar PV and storage sizing by comparing results from the REopt Lite optimization platform from metered data and a set of simulated Department of Energy Commercial Reference Building (CRB) profiles at 65 sites. We find load profile shape alone does not explain a site’s optimal configurations (i.e., PV, Storage, PV and Storage, No System). Still, load profile shape does introduce uncertainty to optimal PV and storage capacities. Across all cases where PV is part of an optimal configuration, we find the average ratio of power capacities derived from metered loads to capacities derived from CRB profiles to be 0.97 (and as high as 1463), where 1 would be a perfect match in system size. For storage, the ratio is 1.6 (and as high as 42). We also assess how, in the absence of complete metered data, a CRB profile can be selected that would be expected to yield the most similar solar PV and storage capacities. From those metrics that can be available from billing data (i.e., peak demand, monthly load totals), we find that uncertainty is most reduced by selecting the CRB’s with an annual peak occurring at the most similar time, or those with the lowest average root mean square error (RMSE) among monthly peak loads. This research can help improve the implementation and interpretation of results derived from simulated load profiles and is an important next step in advancing smart grid solutions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

EI_MS_ML

The unambiguous identification of compounds from their electron ionization mass (EI-MS) spectra remains a significant unsolved problem in the field of metabolomics and analytical chemistry as a whole. Typically EI-MS spectra are compared using various mathematical operations that convert the spectral similarity or differences into a distance-like metric that roughly approximates the similarity of any two spectra. A commonly used metric for this is the cosine similarity metric which has values close to one for very similar spectra and a value of zero for very dissimilar spectra; however, no metric is perfect. Due to the prevalence of structurally-similar compounds such as isomers and the prevalence of certain fragmentation patterns across structurally-dissimilar compounds, the unambiguous assignment of EI-MS spectra compounds remains difficult. Frequently, querying an observed EI-MS spectrum against a large database such as the NIST17 library yields multiple possible assignments requiring the end user to distinguish between multiple high scoring hits, or multiple low scoring hits while keeping in mind that the correct hit may not be in the database at all. Although techniques such as orthogonal information from techniques such as chromatography can greatly aid in unambiguous assignment, this also requires more complicated experimental designs and access to more complicated analytical instrumentation. Substructures can be trivially detected and represented as strings using a previously published technique called node coloring from a known chemical structure. However, for experimentally-derived EI-MS spectra this information must be derived from the spectra itself (i.e., because we do not know what compound it represents). To achieve this, the software uses techniques from the field of machine learning and a large training dataset of EI-MS spectra corresponding to known structures annotated with substructure strings, to build models that can predict the presence of a given chemical substructure from an EI-MS spectrum directly.If these predictions are of high-quality (i.e., are unlikely to be false positives), the presence of one or more predicted substructures can be used to constrain the number of possible hits for a query spectrum. Mathematically, this restriction could be expressed in many forms, but the most straight-forward implementation is to weight the cosine similarity of a query spectrum and a plausible database match with a Tanimoto-like coefficient based on the ratio of the number of substructures predicted to the number of substructures present in the potential database hit. Determining which combination of models best reduces assignment ambiguity will be achieved using a combination of manual curation and optimization techniques such as genetic algorithms. This software will perform all the steps necessary to construct said models from a training dataset and evaluate them using a holdout dataset. Various statistical analyses can be performed to determine if this approach does decrease assignment ambiguity. For example, if this approach works, on average, the rank-order of the correct assignment for the holdout set of EI-MS spectra should decrease and the weighted cosine similarities for most of the possible matches in the database should be better than the unweighted cosine similarities. Furthermore, this same pipeline can be used on real experimental data to generate less ambiguous assignments.

Mitchell, Joshua↗

Support size regulated ruthenium-sulfoacid-nitrogen sites intensify cellulose hydrogenolysis to 1,2-propylene glycol

Rational design of metal-acid-base multifunctional catalysts for upgrading cellulose to 1,2-propylene glycol (1,2-PG) is of great significance for building a sustainable world. However, it is time-consuming and tedious to regulate metal-acid-base sites to balance major reactions to render a high 1,2-PG yield. We herein report support size simultaneously regulated ruthenium-sulfoacid-nitrogen (Ru–SO 3 H–N) sites for cellulose hydrogenolysis to high yield 1,2-PG. Originated from the depolymerization and reassembly of zinc-1,3,5-benzenetricarboxylic acid (ZnBTC) fiber with zeolitic imidazolate framework (ZIF-8) in water, 2-methylimidazole infiltrated nanorod (ZnBTC(mIM)) with a varied aspect ratio was fabricated by varying the feed ratio of ZIF-8/ZnBTC. Upon being pyrolyzed, sulfonated and impregnated with Ru ions, the supported sites were tailored in terms of Ru single-atom/nanocluster ratio, SO 3 H acidity and N basicity. Further, the elaborately fabricated catalyst delivers 32.3% yield of 1,2-PG, corresponding to a high productivity of 67.71 mol h -1 g Ru -1 and a large turnover number of 34193, two and three orders of magnitude higher than those by using other Ru-containing catalytic systems for cellulose hydrogenolysis. The excellent performance can be attributed to optimized electronic and molecular structure of Ru–SO 3 H–N sites that can improve rate-determining cellulose hydrolysis/fructose hydrogenolysis, pivotal glucose isomerization with others to proceed at a matched rate. This study opens a new avenue to facilely tailor the metal-acid-base sites by rational design of size controlled supporting matrix.

1,2-Propylene glycol↗

Calibration of the 2-Phase Bubble Tracking Model for Liquid Mercury Target Simulation with Machine Learning Surrogate Models

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory is one of the most powerful accelerator-driven neutron sources in the world. The intense protons strike on SNS's mercury target to provide bright neutron beams, which also leads to severe fluid-structure interactions inside the target. Prediction of resultant loading on the target is difficult particularly when helium gas is injected into mercury to reduce the loading and mitigate the pitting damage on vessel walls. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this multi-physics problem. However, several uncertain parameters in the R-P model require intensive simulations to determine their optimal values. With the help of machine learning and the measured target strain, we have studied the major uncertain parameters in this R-P model and developed a framework to identify optimal parameters that significantly reduce the discrepancy between simulations and experimental strains. The preliminary results show the possibility of using this mercury/helium mixture and surrogate models to predict a better match of target strain response when the helium gas is injected.

Lin, Lianshan↗

The influence of increasing atmospheric CO 2 , temperature, and vapor pressure deficit on seawater-induced tree mortality

We report increasing seawater exposure is killing coastal trees globally, with expectations of accelerating mortality with rising sea levels. However, the impact of concomitant changes in atmospheric CO 2 concentration, temperature, and vapor pressure deficit (VPD) on seawater-induced tree mortality is uncertain. We examined the mechanisms of seawater-induced mortality under varying climate scenarios using a photosynthetic gain and hydraulic cost optimization model validated against observations in a mature stand of Sitka-spruce (Picea sitchensis) trees in the Pacific Northwest, USA, that were dying from recent seawater exposure. The simulations matched well with observations of photosynthesis, transpiration, nonstructural carbohydrates concentrations, leaf water potential, the percentage loss of xylem conductivity, and stand-level mortality rates. The simulations suggest that seawater-induced mortality could decrease by ~16.7% with increasing atmospheric CO 2 levels due to reduced risk of carbon starvation. Conversely, rising VPD could increase mortality by ~5.6% because of increasing risk of hydraulic failure. Across all scenarios, seawater-induced mortality was driven by hydraulic failure in the first two years after seawater exposure began, with carbon starvation becoming more important in subsequent years. Changing CO 2 and climate appear unlikely to have a significant impact on coastal tree mortality under rising sea levels.

54 ENVIRONMENTAL SCIENCES↗

Kepler Data Analysis: Non-Gaussian Noise and Fourier Gaussian Process Analysis of Stellar Variability

We develop a statistical analysis model of Kepler stellar flux data in the presence of planet transits, non-Gaussian noise, and stellar variability. We first develop a model for the Kepler noise probability distribution in the presence of outliers, which make the noise probability distribution non-Gaussian. We develop a signal likelihood analysis based on this probability distribution, in which we model the signal as a sum of the star variability and planetary transits. We argue that these components need to be modeled together if optimal signal is to be extracted from the data. For the stellar variability model we develop an optimal Gaussian process analysis using a Fourier-based Wiener filter approach, where the power spectrum is non-parametric and learned from the data. We develop high dimensional optimization of the objective function, where we jointly optimize all the model parameters, including thousands of star variability modes, and planet transit parameters. We apply the method to Kepler-90 data and show that it gives a better match to the stellar variability than the existing methods, and robustly handles noise outliers. As a consequence, the planet radii have a higher value than what the existing methods give, including splines and celerite.

79 ASTRONOMY AND ASTROPHYSICS↗

Time-Zone-Pattern Satellite Broadcasting Antenna

Direct-broadcast satellite antenna designs provide contoured beams to match four time zones in 48 contiguous states and spot beams for Alaska, Hawaii, and Puerto Rico presented in 29-page report. Includes descriptions of procedures used to arrive at optimized designs. Arrangements, amplitudes, and phases of antenna feeds presented in tables. Gain contours shown graphically. Additional tables of performance data given for cities in service area of Eastern satellite.

Galindo, Victor↗

Vibration Method for Tracking the Resonant Mode and Impedance of a Microwave Cavity

There has been a concerted effort to improve the microwave heating technique for processing materials at high temperature. Improved optimization of the energy transfer from a microwave source to the material being processed is required. A vibration technique is pre- sented to continuously maintain a mode resonance and impedance match between a constant frequency magnetron source and a resonant cavity.

microwave↗

Optimization of switch diagnostics on the MAIZE linear transformer driver

The MAIZE Linear Transformer Driver is made of 40 capacitor-switch-capacitor `bricks' connected in parallel. When these 40 bricks are charged to 100-kV and then discharged synchronously, the MAIZE facility generates a 1-MA current pulse with a 100-ns rise time into a matched load impedance. Discharging each of the capacitors in a brick is carried out by the breakdown of a spark-gap switch, a process which results in the emission of light. Monitoring this output light with a fiber optic coupled to a photomultiplier tube (PMT) and an oscilloscope channel provides information on switch performance and timing jitter– whether a switch red early, late, or in phase with the other switches. However, monitoring each switch with a dedicated detector- oscilloscope channel can be problematic for facilities where the number of switches to be monitored (e.g., 40 on MAIZE) greatly exceeds the number of detector-oscilloscope channels available. The technique of using fibers to monitor light emission from switches can be optimized by treating a PMT as a binary digit or bit and using a combinatorial encoding scheme, where each switch is monitored by a unique combination of fiber- PMT-oscilloscope channels simultaneously. By observing the unique combination of ber-PMT-oscilloscope channels that are turned on, the pre-firing or late-firing of a single switch on MAIZE can be identified by as few as six PMT-oscilloscope channels. The number of PMT-oscilloscope channels, N , required to monitor X switches can be calculated by 2 N = X + 1, where the number '2' is selected because the PMT-oscilloscope acts as a bit. Here, we demonstrate the use of this diagnostic technique on MAIZE. In conclusion, we also present an analysis of how this technique could be scaled to monitor the tens of thousands of switches proposed for various next generation pulsed power facilities.

47 OTHER INSTRUMENTATION↗